Wearable electronic device and system for tracking location and identifying changes in salient indicators of patient health

ABSTRACT

A wearable electronic device, a system and methods of monitoring with a wearable electronic device. The device includes a hybrid wireless communication module with wireless communication sub-modules to selectively acquire location data from both indoor and outdoor sources, as well as a wireless communication sub-module to selectively transmit an LPWAN signal to provide location information based on the acquired data. The device may also include one or more sensors to collect one or more of environmental data, activity data and physiological data. The device may transmit some or all of its acquired data to a larger system, including a cloud-based server to, in addition to providing location-based data, be used as a part of a predictive health care protocol to correlate changes in acquired data to salient indicators of the health of a wearer of the device. In one form, the predictive health care protocol uses a machine learning model.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to and is a divisional of pending U.S.patent application Ser. No. 16/233,462, filed on Dec. 27, 2018 thatclaims priority to U.S. Provisional Application Ser. No. 62/709,126 thatwas filed on Jan. 5, 2018.

The present disclosure relates generally to a wearable electronic deviceand corresponding system for monitoring one or more of location,environmental, activity and physiological (LEAP) data of a wearer of thedevice, and more particularly to a wearable electronic device thatwirelessly communicates such data in a hybrid mode in order to allowsuch data to be used to proactively identify salient indicators ofchanging health of the wearer of the device.

BACKGROUND

Dementia—such as Alzheimer's Disease, Parkinson's Disease and relatedneurodegenerative conditions—corresponds to a decline in mental abilitysevere enough to interfere with one's daily life, including theactivities of daily living (ADL). Over five million people suffer fromdementia in the United States alone, and this number is predicted toincrease.

One problem in caring for those suffering from dementia is that they maybecome confused of their surroundings and tend to wander and get lost.If these individuals are not located in a timely manner, they are atrisk of injury. To compound the problem, many of the individualssuffering from dementia will not have the mental acuity to remembertheir name, place of residence or other identifying indicia even in theevent that they do wander and encounter someone trying to assist them.

Another problem in caring for those suffering from dementia is thattheir decline is often accompanied by corresponding declines in mentalor physical health, including the elderly and those experiencing earlyonset. For example, individuals suffering from dementia may be prone toinfections, pneumonia, neuropsychiatric symptoms or other comorbidities.Furthermore, these declines may not manifest themselves until theaccompanying comorbidity is relatively advanced, as the person sufferingfrom the condition may not be able to articulate symptoms that ifotherwise identified early enough could be used to provide suitablemedical intervention. One form of infection that afflicts the elderly ingeneral and those suffering from dementia in particular is a urinarytract infection (UTI). Unfortunately, information that could indicatethe presence of a UTI in such patients is difficult, time-consuming andexpensive to acquire, requiring frequent or continuous monitoring by acaregiver of the patient's activities in order to determine if a UTI isimminent or present.

The problems associated with caring for an individual with such mentaland physical conditions is exacerbated in situations where thecaregiver—whether a doctor, nurse, therapist, home care aide, familymember, friend or the like—is not able to be with the individual at alltimes of the day and night in order to determine whether the individualis symptomatic. For example, if the individual is still living at homeby himself or herself, it is possible that extended periods of time maypass before health-related symptoms may be made known to the caregiver.Moreover, caring for persons that are suffering from—or are manifestingearly signs of—either or both of mental and physical frailties isparticularly difficult in group settings such as nursing homes, assistedliving communities or related long-term health care centers, owing atleast in part to the small number of staffed caregivers relative to thenumber of patients residing within.

SUMMARY

The devices, systems and methods of the present disclosure utilize awearable, wireless application that improves the ability to track thelocation and associated environment, activity and physiologicalinformation of a person that is suffering from—or is manifestingsymptoms associated with—dementia, infections, neuropsychiatric problemsor other adverse health conditions in order to provide data-informedcare insights for family members, nurses, doctors or other caregivers.Such devices, systems and methods may be used to supplant conventionaldata acquisition components and associated computer systems by promotingthe collection of large amounts of data needed for such insights, alongwith the ability to then disseminate such data to remote locations overlarge geographic areas for extended periods of time without the need forcomplicated communications infrastructure. This solves the problem thatis particularly acute in conventional data-acquiring devicearchitectures that require significant amounts of electrical energyconsumption in order to receive, process and transmit such data that isdue at least in part to the seemingly incompatible goals of attaininglong battery life and extended transmission range.

For example, devices capable of sending data over wide areas (such asthat associated with a cellular-based architecture) tend to consumelarge amounts of power, while devices capable of long battery life (suchas those that receive and send using conventional Bluetooth andWiFi-based approaches) tend to have detection and transmission rangesthat make them unsuitable for tracking the location of a wearer of thedevice either indoors within multi-room dwellings or outdoors over largegeographic areas. The shortcomings associated with conventionalcomputerized data acquisition approaches are remedied by the specificimplementation of the data acquisition, computer structure andcommunication configuration according to the present disclosure.

According to a first aspect of the present disclosure, a wearableelectronic device includes a platform configured to be secured to aperson, a source of electric current and a hybrid wireless communicationmodule supported by the platform and receiving electric power from thesource of electric current. The hybrid wireless communication moduleincludes various sub-modules made up of a first sub-module toselectively receive location data in the form of a beacon signal, asecond sub-module to selectively receive location data in the form of aglobal navigation satellite system (GNSS) signal and a third sub-moduleto transmit a low-power wide area network (LPWAN) signal that providesat least location indicia of the wearable electronic device based onacquired location data from at least one of the first and secondwireless communication sub-modules. Within the present context, thereceipt and transmission of location data is understood to be selectiveinsofar as an incoming signal (in the case of received location data)from a remote source is detectable by the wearable electronic device, orwhen an outgoing LPWAN signal (in the case of transmitted location data)from the wearable electronic device is detectable by a remote gateway orrelated receiver. As such, even though the hybrid wireless communicationsystem may in one form involve the periodic or continuous sending orreceipt of data, because the corresponding sources or recipients of suchdata may in certain circumstances be out of range, such receipt andtransmission must—out of necessity—be deemed selective. In one form, thebeacon signals used to provide the location data associated with thewearable electronic device include those from near-range,private-network infrastructure (i.e., those that do not require cellularor related public-network features in order to send and receive wirelesssignals) such as a Bluetooth Low Energy (BLE, also referred to asBluetooth low energy technology) network for indoor operation. Inaddition, signals used to provide location data to the first wirelesscommunication sub-module may include those from GNSS in order to satisfyoutdoor, long-range location needs.

In one form, an embodiment of the first aspect may include numeroussensors, a non-transitory computer readable medium, a processor that isconfigured to perform a predefined set of operations in response toreceiving a corresponding instruction selected from a predefined nativeinstruction set, and a set of machine codes selected from the nativeinstruction set and operated upon by the processor to receive ortransmit LEAP data.

In one form, an embodiment of the first aspect may include one or moreof the previous forms, in which another machine code determines adistance between the wearable electronic device and a source of thebeacon signal.

In one form, an embodiment of the first aspect may include one or moreof the previous forms, in which the machine code that transmits at leasta portion of the LEAP data through the third wireless communicationsub-module is used to convey such data through the third wirelesscommunication sub-module while the data is in substantially unprocessedform.

In one form, an embodiment of the first aspect may include one or moreof the previous forms, in which the machine code that transmits at leasta portion of the received LEAP data through the third wirelesscommunication sub-module is used to convey such data through the thirdwireless communication sub-module while the data is in substantiallyprocessed form.

In one form, an embodiment of the first aspect may include one or moreof the previous forms, in which the machine code that transmits at leasta portion of the received LEAP data through the third wirelesscommunication sub-module is used to convey such data through the thirdwireless communication sub-module while the data is in partiallyprocessed form.

In one form, an embodiment of the first aspect may include one or moreof the previous forms, in which at least one of the sensors that areconfigured to detect physiological data includes at least one sensorselected from the group consisting of a heart rate sensor, a breathingrate sensor, a temperature sensor, a respiration sensor, a pulseoximetry sensor, a respiratory rate sensor, an oxygen saturation sensor,an electrocardiogram sensor, a cardiac output index sensor, a systematicpressure sensor, a systematic systolic arterial pressure sensor; asystematic diastolic arterial pressure sensor; a systematic meanarterial pressure sensor, a central venous pressure sensor, a pulmonarypressure sensor, a pulmonary systolic arterial pressure sensor, apulmonary diastolic arterial pressure sensor, a pulmonary mean arterialpressure sensor and a smell sensor.

In one form, an embodiment of the first aspect may include one or moreof the previous forms, in which at least one of the sensors that areconfigured to detect activity data comprises at least one sensorselected from the group consisting of an accelerometer, a gyroscope, amagnetometer, an altimeter, a motion detector and an inertialmeasurement unit.

In one form, an embodiment of the first aspect may include one or moreof the previous forms, wherein at least one of the sensors that areconfigured to detect environmental data includes at least one sensorselected from the group consisting of an ambient temperature sensor, anambient pressure sensor, an ambient humidity sensor, an ambient lightsensor, a motion sensor, a carbon monoxide sensor, a carbon dioxidesensor, a smoke detector and a microphone.

In one form, an embodiment of the first aspect may include one or moreof the previous forms, further including a nurse call button that issupported by the platform and signally cooperative with the thirdwireless communication sub-module such that upon activation of the nursecall button, at least one signal is transmitted from the wearableelectronic device over the third wireless communication sub -module.

In one form, an embodiment of the first aspect may include one or moreof the previous forms, in which another machine code maintains thesecond wireless communication sub-module in a sleep mode until detectionof either a waking event or a set period of time.

In one form, an embodiment of the first aspect may include one or moreof the previous forms, wherein the waking event includes detecting thatthe wearable electronic device is outside of a range of detection of thefirst wireless communication sub-module.

In one form, an embodiment of the first aspect may include one or moreof the previous forms, in which another machine code controls thetransmission of data from at least two forms of the received wearableelectronic device LEAP data through the third wireless communicationsub-module.

In one form, an embodiment of the first aspect may include one or moreof the previous forms, in which wherein the hybrid wirelesscommunication module does not include a cellular wireless communicationsub-module.

In one form, an embodiment of the first aspect may include one or moreof the previous forms, wherein the platform is selected from the groupconsisting of a wrist-worn band, an ankle-worn band, an article ofclothing (such as outerwear, underwear, a belt, a shoe or the like), abandage, a pair of eyeglasses, a necklace or pendant, aclothing-affixable pin, a clothing-affixable patch or a subcutaneousimplant.

In one form, an embodiment of the first aspect may include one or moreof the previous forms, wherein the first wireless communicationsub-module selectively receives location data in the form of a BLEsignal.

According to a second aspect of the present disclosure, a system fortracking the location of an individual is disclosed. The system includesa wearable electronic device that includes a platform, and one or moreof a battery, solar cell, capacitive device or other source of electriccurrent, as well as a hybrid wireless communication module made up of atleast first, second and third wireless communication sub-modules. Thesystem additionally includes a gateway in wireless signal communicationwith the third wireless communication sub-module. The platform isconfigured to be worn or otherwise secured to the individual, while thehybrid wireless communication module is structurally supported by theplatform and can receive electric power from the source of electriccurrent. The first wireless communication sub-module selectivelyreceives location data in the form of a beacon signal, the secondwireless communication sub-module selectively receives location data inthe form of a GNSS signal, while the third wireless communicationsub-module transmits an LPWAN signal that provides location indicia ofthe wearable electronic device based on acquired location data from atleast one of the first and second wireless communication sub-modules.

In one form, an embodiment of the second aspect may include having thewearable electronic device and the gateway cooperate with one another aspart of a star (rather than mesh) topology network.

In one form, an embodiment of the second aspect may include one or moreof the previous forms, and may further include a backhaul server insignal communication with the gateway.

In one form, an embodiment of the second aspect may include one or moreof the previous forms, and may be configured such that one or both ofthe wearable electronic device and the backhaul server include anon-transitory computer readable medium, a processor and a set ofmachine codes selected from a native instruction set and operated uponby the processor. In addition, at least a portion of the set of machinecodes are stored in the respective non-transitory computer readablemedium or mediums. Data structures are made to correspond to variousnodes of a machine learning model, such as the input, intermediate andoutput nodes of a neural network or the input and output nodes of aK-means clustering approach, and in one form may be implemented in thememory of the respective computer readable medium or mediums. Such datastructures may be operated upon by a search algorithm implemented inmachine codes for the respective one of the wearable electronic deviceprocessor or backhaul server processor.

In one form, an embodiment of the second aspect may include one or moreof the previous forms, and may further include at least one beacon tocommunicate with the first wireless communication sub-module.

According to a third aspect of the present disclosure, a system foranalyzing the health condition of an individual is disclosed. The systemincludes a wearable electronic device made up of a platform configuredto be worn or otherwise secured to a person, a source of electriccurrent and a hybrid wireless communication module structurallysupported by the platform and receiving electric power from the battery.The hybrid wireless communication system includes a first wirelesscommunication sub-module that during its operation selectively receiveslocation data in the form of a beacon signal, a second wirelesscommunication sub-module that during its operation selectively receiveslocation data in the form of a GNSS signal, and a third wirelesscommunication sub-module that during its operation transmits anLPWAN-based signal that provides location indicia of the wearableelectronic device based on acquired location data from at least one ofthe first and second wireless communication sub-modules. Numeroussensors are supported by the platform such that during operation, atleast one sensor detects a respective one of environmental data,activity data and physiological data from an individual to whom thewearable electronic device is secured. Additional components of thesystem include a gateway and a backhaul server. The gateway is inwireless signal communication with the third wireless communicationsub-module, while the backhaul server is in signal communication withthe gateway. Furthermore, the wearable electronic device and thebackhaul server have at least a non-transitory computer readable medium,processor and set of machine codes selected from the native instructionset and capable of being operated upon by a respective one theprocessors in response to at least a portion of the set of machine codesthat are stored in one, the other or both of the non-transitory computerreadable mediums. As such, at least one of the backhaul server and thewearable electronic device has a machine code to execute a machinelearning model to classify the health condition of the individual basedat least in part on at least a portion of the LEAP data.

In one form, an embodiment of the third aspect may include having themachine learning model be a neural network that includes numerous inputnodes each of which includes a memory location for storing an inputvalue that corresponds to a portion of a respective one of the acquiredLEAP data, numerous hidden nodes each of which is connected to at leastone of the plurality of input nodes and includes computationalinstructions, implemented in machine codes of the respective processor,for computing a plurality of output values, respectively, and numerousoutput nodes each of which includes a memory location for storing theoutput value produced by the machine learning classification model.

In one form, an embodiment of the third aspect may include one or moreof the previous forms, wherein at least a portion of the set of machinecodes that are on at least one of the non-transitory computer readablemedium of the wearable electronic device and the non-transitory computerreadable medium of the backhaul server and that are operated upon by therespective processor further includes a machine code that compares atleast a portion of the LEAP data to baseline data that forms a datastructure that is stored on one or both of the non-transitory computerreadable medium of the wearable electronic device and the non-transitorycomputer readable medium of the backhaul server.

In one form, an embodiment of the third aspect may include one or moreof the previous forms, wherein the baseline data is selected from thegroup consisting of baseline data of the individual and baseline data ofa demographic group representative of the individual.

In one form, an embodiment of the third aspect may include one or moreof the previous forms, wherein at least a portion of the set of machinecodes that are on one or both of the non-transitory computer readablemedium of the wearable electronic device and the non-transitory computerreadable medium of the backhaul server and that are operated upon by therespective processor further comprises a machine code to provideclinical decision support through the machine learning classificationmodel.

In one form, an embodiment of the third aspect may include one or moreof the previous forms, further including a machine code to provide adiagnosis through the machine learning classification model.

In one form, an embodiment of the third aspect may include one or moreof the previous forms, further including a machine code to execute anADL analysis of the individual using at least a portion part of the LEAPdata.

In one form, an embodiment of the third aspect may include one or moreof the previous forms, further including a machine code to send an alertthrough the third wireless communication sub-module when the transmittedlocation data indicates that an individual is situated outside apredetermined area.

In one form, an embodiment of the third aspect may include one or moreof the previous forms, further including machine code to train themachine learning classification model, the machine code to train themachine learning classification model comprising (a) a machine code tocleanse at least a portion of the LEAP data, (b) a machine code toextract at least one feature vector from the cleansed data, and (c) amachine code to execute at least one algorithm based on the one or morefeature vectors a machine code to execute at least one algorithm basedon the at least one feature vector such that upon execution of the atleast one algorithm, the machine learning classification model isconfigured provide a predictive analytical output of the healthcondition to a caregiver.

In one form, an embodiment of the third aspect may include one or moreof the previous forms, wherein at least a portion of the machine code totrain the machine learning model includes a machine code to segment atleast a portion of at least one of the LEAP data into a training dataset, a validation data set and a testing data set, as well as a machinecode that refines the machine learning model based on the execution ofthe at least one algorithm on the training, validation and testing datasets.

In one form, an embodiment of the third aspect may include one or moreof the previous forms, wherein the machine code to train the machinelearning classification model is cooperative with the machine learningclassification model through (a) a machine code that analyzes at least aportion of at least one of the location data, environmental data,activity data and physiological data once the machine learningclassification model has been trained by the at least one algorithm, and(b) a machine code that causes at least one of the wearable electronicdevice and the backhaul server to output the analyzed data (such as toat least one of memory, a display, an audio alert or other use by acaregiver).

According to a fourth aspect of the present disclosure, a method ofmonitoring an individual with a wearable electronic device is disclosed.The method includes acquiring, using the wearable electronic device,location data from at least one of BLE location data and GNSS locationdata, and wirelessly transmitting the location data from the wearableelectronic device to a wireless LPWAN receiver using a star topologynetwork.

In one form, an embodiment of the fourth aspect may include acquiring,with numerous sensors that are formed as part of the wearable electronicdevice, at least one of environmental data, activity data andphysiological data, and wirelessly transmitting such data from thewearable electronic device to the wireless LPWAN receiver.

In one form, an embodiment of the fourth aspect may include one or moreof the previous forms, wherein the wirelessly transmitting of the LEAPdata is performed using a hybrid wireless communication module thatforms a part of the wireless electronic device.

In one form, an embodiment of the fourth aspect may include one or moreof the previous forms, further including determining, using a machinelearning model, a health condition of the individual, as well asproviding an output of the health condition. The machine learning modelis based at least in part on at least a portion of the LEAP data.

In one form, an embodiment of the fourth aspect may include one or moreof the previous forms, wherein the determining includes operating atleast a non-transitory computer readable medium, a processor and a setof machine codes selected from a native instruction set such that atleast a portion of the set of machine codes are stored in thenon-transitory computer readable medium. The set of machine codesincludes a machine code that cleanses at least a portion of the acquiredLEAP data, a machine code that extracts at least one feature vector fromthe cleansed data, a machine code that trains at least one machinelearning algorithm using the one or more feature vectors, a machine codethat analyzes at least a portion of at least one of the LEAP data oncethe machine learning model has been trained by the at least onealgorithm, and a machine code that causes the analyzed data to be outputa caregiver.

In one form, an embodiment of the fourth aspect may include one or moreof the previous forms, wherein the machine code to train the machinelearning classification model includes a machine code to segment atleast a portion of at least one of the LEAP data into a training dataset for use by the at least one machine learning algorithm, a machinecode that segments at least a portion of at least one of the LEAP datainto a validation data set for use by the at least one machine learningalgorithm, a machine code that segments at least a portion of at leastone of the LEAP data into a testing data set for use by the at least onemachine learning algorithm, and a machine code that refines the machinelearning classification model based on the execution of the at least onemachine learning algorithm on the training, validation and testing datasets.

In one form, an embodiment of the fourth aspect may include one or moreof the previous forms, wherein the machine learning model is executed onat least one of the wearable electronic device, a backhaul server andcloud.

In one form, an embodiment of the fourth aspect may include one or moreof the previous forms, wherein the health condition is an adverse healthcondition selected from the group consisting of an infection and aneuropsychiatric condition.

In one form, an embodiment of the fourth aspect may include one or moreof the previous forms, wherein the infection comprises a UTI such thatat least a portion of the set of machine codes that are on at least oneof the non-transitory computer readable medium of the wearableelectronic device and the non-transitory computer readable medium of thebackhaul server and that are operated upon by the respective processorfurther includes a machine code that executes at least a portion of aMcGeer Criteria analysis based on at least a portion of the LEAP data.

In one form, an embodiment of the fourth aspect may include one or moreof the previous forms, wherein the machine code that executes at least aportion of a McGeer Criteria analysis determines at least one of (a) newor marked increase in urination urgency, (b) new or marked increase inurination frequency, (c) new or marked increase in incontinence and (d)change in functional status, based at least in part on at least aportion of the LEAP data.

In one form, an embodiment of the fourth aspect may include one or moreof the previous forms, wherein the infection comprises a pneumonia suchthat at least a portion of the set of machine codes that are on at leastone of the non-transitory computer readable medium of the wearableelectronic device and the non-transitory computer readable medium of thebackhaul server and that are operated upon by the respective processorfurther includes a machine code that executes a pneumonia analysis basedat least in part on at least a portion of the LEAP data.

In one form, an embodiment of the fourth aspect may include one or moreof the previous forms, wherein the machine code to execute at least aportion of a pneumonia analysis comprises machine code to execute atleast a portion of at least one of a PSI score, CURB-65 score, SMART-COPscore and an A-DROP score based on at least one of the location data andactivity data in conjunction with the physiological data that comprisesat least one of respiratory data and heat rate data. These acronyms willbe further defined in more detail later in this disclosure, where thepneumonia analysis may further include one or more factors from each ofthese scores.

In one form, an embodiment of the fourth aspect may include one or moreof the previous forms, wherein the infection comprises influenza suchthat at least a portion of the set of machine codes that are on at leastone of the non-transitory computer readable medium of the wearableelectronic device and the non-transitory computer readable medium of thebackhaul server and that are operated upon by the respective processorfurther comprises a machine code to execute an influenza analysis basedon at least a portion of at least one of the location data,environmental data, activity data and physiological data.

In one form, an embodiment of the fourth aspect may include one or moreof the previous forms, wherein the machine code to execute at least aportion of an influenza analysis comprises machine code to execute atleast a portion of an influenza score based on at least one of thelocation data and activity data in conjunction with the physiologicaldata that comprises at least one of temperature data, respiratory dataand heat rate data.

In one form, an embodiment of the fourth aspect may include one or moreof the previous forms, wherein the neuropsychiatric condition includesagitation such that at least a portion of the set of machine codes thatare on at least one of the non-transitory computer readable medium ofthe wearable electronic device and the non-transitory computer readablemedium of the backhaul server and that are operated upon by therespective processor further comprises a machine code to execute anagitation analysis based on at least a portion of at least one of thelocation data, environmental data, activity data and physiological data.

In one form, an embodiment of the fourth aspect may include one or moreof the previous forms, wherein the machine code to execute an agitationanalysis comprises machine code to determine whether the individual ispacing.

In one form, an embodiment of the fourth aspect may include one or moreof the previous forms, wherein the neuropsychiatric condition includescognitive impairment such that at least a portion of the set of machinecodes that are on at least one of the non-transitory computer readablemedium of the wearable electronic device and the non-transitory computerreadable medium of the backhaul server and that are operated upon by therespective processor further comprises a machine code to execute acognitive impairment analysis based on at least a portion of at leastone of the location data, environmental data, activity data andphysiological data.

In one form, an embodiment of the fourth aspect may include one or moreof the previous forms, wherein the machine code to execute at least aportion of a cognitive impairment condition analysis comprises machinecode to determine at least one of a stage of dementia selected from thegroup consisting of at least one of (a) early stage dementia, (b)moderate stage dementia, (c) late stage dementia and (d) terminal stagedementia.

In one form, an embodiment of the fourth aspect may include one or moreof the previous forms, wherein indicia of the at least one stages ofdementia is determined by at least one of wherein indicia of the earlystage dementia is selected from the group consisting of lack ofinitiation of activities, confusion about places and times, inordinateamount of loss of personal items, withdrawn personality, negative changein at least one activities of daily living event or instrumentalactivities of daily living event, increase in irritability; indicia ofthe moderate stage dementia is selected from the group consisting ofincrease in suspicion of others, increase in an amount of full-timesupervision, increased need of assistance with mobility, heightenedproblems with reading, writing and performing calculations, making upstories in order to fill in the increasingly frequent gaps in memory,loss of impulse control, emotional lability, restlessness, sloppiness,outbursts of anger, frequent sleeping, nighttime wandering, incontinenceof urine, childlike behavior, paranoia, diminished social activity,increased frequency of falls, increased frequency of falls in attemptingto transfer from one place or position to another; indicia of the latestage dementia is selected from the group consisting of complete lack ofambulatory activity, having poor safety awareness, increased rate ofmental decline after an acute hospitalization event, forgetting when alast meal was eaten, little capacity for self-care, requirement for helpwith all bathing, dressing and eating activities, loss of bowel control,prone to making verbal utterances not related to pain, increased amountof sleeping, difficulty in communicating with words, difficulty withliquids, coughing after taking a drink, lack of appetite and weight losseven with a good diet; and indicia of the terminal stage dementia isselected from the group consisting of the patient experiencing recurrentaspirations even with thick liquids, pressure ulcers even with frequentturnings and related good quality of care, as well as unawareness ofexternal stimuli.

In one form, an embodiment of the fourth aspect may include one or moreof the previous forms, wherein the neuropsychiatric condition isanalyzed through a regression-based machine learning model.

According to a fifth aspect of the present disclosure, a method of usinga machine learning model to evaluate a health condition of an individualis disclosed. The method includes acquiring, using a wearable electronicdevice, location data from one or both of BLE location data and GNSSlocation data and acquiring, with a plurality of sensors that are formedas part of the wearable electronic device, at least one of environmentaldata, activity data and physiological data. The method also includeswirelessly transmitting at least a portion of the LEAP data from thewearable electronic device to a wireless low power wide area networkreceiver using a star topology network, as well as executing a machinelearning model based at least in part on at least a portion of the LEAPdata. The executing takes place using a non-transitory computer readablemedium, a processor and a set of machine codes within a predefinednative instruction set. In this way, a predefined set of operations isperformed by the processor in response to receiving a correspondinginstruction selected from the set of machine codes that includes amachine code that analyzes at least a portion of at least one of theLEAP data once the machine learning model has been trained by at leastone algorithm, and a machine code that causes the analyzed data to beoutput to a caregiver.

In one form, an embodiment of the fifth aspect may include one or moreof the previous forms, wherein the machine learning model is trained bythe at least one machine learning algorithm that includes a machine codethat cleanses at least a portion of at least one of the LEAP data, amachine code that extracts at least one feature vector from the cleanseddata, and a machine code that executes the at least one machine learningalgorithm using the at least one feature vector.

In one form, an embodiment of the fifth aspect may include one or moreof the previous forms, wherein the machine learning model comprises anunsupervised approach that comprises K-means clustering.

In one form, an embodiment of the fifth aspect may include one or moreof the previous forms, wherein the machine learning model comprises anunsupervised approach that comprises a neural network.

In one form, an embodiment of the fifth aspect may include one or moreof the previous forms, wherein the machine learning model comprises ahybrid approach that comprises a supervised approach in cooperation withan unsupervised approach.

In one form, an embodiment of the fifth aspect may include one or moreof the previous forms, wherein the supervised approach comprises aneural network and the unsupervised approach comprises K-meansclustering.

According to a sixth aspect of the present disclosure, a method ofperforming cognitive assessment of an individual is disclosed. Themethod includes receiving data from a plurality of sensors of a wearableelectronic device that is secured to the individual, the datacorresponding to at least a portion of LEAP data, converting such datainto a labeled feature vector that describes at least one attribute ofthe data, comparing the labeled feature vector to baseline data using amachine learning model, determining a cognitive status of the individualbased on an output from the machine learning algorithm, and conveyingthe cognitive status to a caregiver.

In one form, an embodiment of the sixth aspect may include performing anADL analysis that uses at least a portion of the LEAP data as input.

According to a seventh aspect of the present disclosure, anon-transitory computer readable medium is disclosed. The medium hasexecutable instructions thereon that when executed on a machine causethe machine to receive location data from at least one of a beaconsignal and a GNSS signal through a hybrid wireless communication modulethat forms at least a part of a wearable electronic device, and transmitthe location data as an LPWAN signal through the wireless communicationmodule.

In one form, an embodiment of the seventh aspect may further includecausing the machine to acquire sensed activity, environmental andphysiological data from at least one sensor that forms at least a partof the wearable electronic device.

In one form, an embodiment of the seventh aspect may include one or moreof the previous forms, wherein the executable instructions furthercauses the machine to use at least a portion of the LEAP data in orderto determine whether an individual associated with the wearableelectronic device is at an increased risk of developing an adversehealth condition.

In one form, an embodiment of the seventh aspect may include one or moreof the previous forms, wherein the executable instructions furthercauses the execution of a machine learning model that (a) is trainedusing at least at least one training algorithm along with at least aportion of the LEAP data and (b) analyzes at least a portion of the LEAPdata once such machine learning classification algorithm has beentrained to output indicia of such increased risk of developing anadverse health condition based on results generated by the machinelearning model.

In one form, an embodiment of the seventh aspect may include one or moreof the previous forms, wherein the indicia of such increased risk ofdeveloping an adverse health condition is based at least in part on acomparison of at least a portion of the LEAP data to baseline data.

According to an eighth aspect of the present disclosure, a wearableelectronic device for tracking a patient is disclosed. The deviceincludes a processor, a power source electrically connected to theprocessor, a first wireless communication sub-module with a BLE chipcommunicatively connected to the processor and configured to receive BLElocation information, a second wireless communication sub-module with aGNSS chip communicatively connected to the processor and configured toreceive GNSS location information, and a third wireless communicationsub-module with an LPWAN chip communicatively connected to theprocessor. The third wireless communication sub-module provides locationindicia of the device based on location information from at least one ofthe first and second wireless communication sub-modules.

According to another aspect of the present disclosure, a computerizedmethod of using a wearable electronic device that includes determiningwhether a patient is at risk of developing a UTI is disclosed. Accordingto still another aspect of the present disclosure, a computerized methodof determining whether a patient is at risk of developing an adversehealth condition based on an ADL determination is disclosed. Accordingto yet another aspect of the present disclosure, a computerized methodof determining whether a patient is demonstrating increased agitation isdisclosed. According to yet another aspect of the present disclosure,any of the previous aspects may include wirelessly transmitting the dataacquired from the wearable electronic device to a wireless LPWANreceiver (such as the gateway) using the star (rather than mesh)topology network. According to still another aspect of the presentdisclosure, a radio signal strength indication (RSSI)-based transmissionprotocol from one or more beacons to the wearable electronic device maybe replaced with another direction-finding protocol such as those basedon angle of arrival (AOA), angle of departure (AOD) or related approachto deliver real-time location functionality. According to still anotheraspect of the present disclosure, one or more machine learning modelssuch as neural networks, K-means clustering or related approaches may beused to analyze the LEAP data acquired by the wearable electronicdevice. It will be appreciated that the number of aspects and theirrespective use of the components and configurations is not limited tothose enumerated above, and that others will be apparent from thetotality of the present disclosure.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The following detailed description of specific embodiments of thepresent disclosure can be best understood when read in conjunction withthe following drawings, where like structure is indicated with likereference numerals and in which:

FIG. 1 depicts a simplified view of wireless signal connectivity betweena wearable electronic device and other parts of a patient monitoringsystem according to one or more embodiments shown or described herein;

FIG. 2A depicts an upper perspective view of the wearable electronicdevice of FIG. 1 according to one or more embodiments shown or describedherein;

FIG. 2B depicts a lower perspective view of a main housing assembly ofthe wearable electronic device of FIG. 2A with a support tray in anas-assembled condition according to one or more embodiments shown ordescribed herein;

FIG. 2C depicts views an upper perspective view of the main housingprior to attachment to the support tray of FIG. 2B according to one ormore embodiments shown or described herein;

FIGS. 2D and 2E depict upper perspective views of the main housingassembly of FIG. 2C in a partially-assembled state with antennas priorto (FIG. 2D) and after (FIG. 2E) connection therebetween according toone or more embodiments shown or described herein;

FIG. 2F depicts an exploded upper perspective view of the wearableelectronic device of FIG. 2A, as well as a block diagrammaticrepresentation of the logic device, various sensors and hybrid wirelesscommunication module according to one or more embodiments shown ordescribed herein;

FIG. 2G depicts a top view of the main housing assembly of the wearableelectronic device of FIG. 2A with the top cover removed according to oneor more embodiments shown or described herein;

FIG. 2H depicts an upper perspective view of the wearable electronicdevice of FIG. 2A with an attachable strap according to one or moreembodiments shown or described herein;

FIG. 2I depicts a perspective view of an alternate embodiment of thewearable electronic device where it is formed in a bandage-likeflexible, hybrid manner for direct affixing to a wearer's skin orarticle of clothing according to one or more embodiments shown ordescribed herein;

FIGS. 3A and 3B depict notional cloud-based uplink and downlink messagesbetween the wearable electronic device and system of FIG. 1;

FIG. 3C depicts a BLE an eleven byte beacon data format for some of themessages of FIGS. 3A and 3B;

FIG. 3D depicts an eleven byte GNSS data format for some of the messagesof FIGS. 3A and 3B;

FIG. 3E depicts an eleven byte battery data format for some of themessages of FIGS. 3A and 3B;

FIG. 3F depicts an eleven byte nurse call button data format for some ofthe messages of FIGS. 3A and 3B;

FIG. 3G depicts an eleven byte nurse response data format for some ofthe messages of FIGS. 3A and 3B;

FIG. 3H depicts an eleven byte band worn data format for some of themessages of FIGS. 3A and 3B;

FIG. 3I depicts an eleven byte band removed data format for some of themessages of FIGS. 3A and 3B;

FIG. 3J depicts an eleven byte no movement data format for some of themessages of FIGS. 3A and 3B;

FIG. 3K depicts an eleven byte movement data format for some of themessages of FIGS. 3A and 3B;

FIG. 4 depicts a simplified view of initialization, maintenance andcharging of the wearable electronic device of FIG. 1 according to one ormore embodiments shown or described herein;

FIG. 5 depicts a simplified view of a cloud-based connectivity betweenvarious interested parties receiving information from the patientlocation and activity monitoring system of FIG. 1 according to one ormore embodiments shown or described herein;

FIG. 6 depicts a program structure in the form of a flow diagram of howthe wearable electronic device and system of FIG. 1 may be used todevelop a machine learning model according to one or more embodimentsshown or described herein;

FIG. 7 depicts a program structure in the form of a neural networkaccording to one or more embodiments shown or described herein;

FIG. 8 depicts a data structure in the form of a portion of a samplepatient ADL documentation chart that may be automated through datagathered by the wearable electronic device and system of FIG. 1according to one or more embodiments shown or described herein;

FIG. 9 depicts the wearable electronic device and apportion of thesystem of FIG. 1 and their wireless connectivity through the cloud toascertain the location and activity of a patient within a multi-patientdwelling, as well as to provide patient information in display form to aremote computing device according to one or more embodiments shown ordescribed herein;

FIG. 10A depicts a notional dashboard that can be displayed to acaregiver on the remote computing device of FIG. 9 to identify aparticular patient, along with the patient's planned and recentactivities based on LEAP data that is generated by the wearableelectronic device and system of FIG. 1 according to one or moreembodiments shown or described herein;

FIG. 10B depicts a notional dashboard that can be displayed to acaregiver on the remote computing device of FIG. 9 to identify aparticular patient, along with a bar chart form of the patient's dailybathroom visits and a weekly comparison based on LEAP data that isgenerated by the wearable electronic device and system of FIG. 1according to one or more embodiments shown or described herein;

FIG. 10C depicts a notional dashboard that can be displayed to acaregiver on the remote computing device of FIG. 9 to identify the dailyfrequency of room visits by a particular patient over the course of aweek and that is based on LEAP data that is generated by the wearableelectronic device and system of FIG. 1 according to one or moreembodiments shown or described herein;

FIG. 10D depicts in bar chart form a notional dashboard that can bedisplayed to a caregiver on the remote computing device of FIG. 9 toidentify the amount of time that a particular patient spends in variousrooms over the course of a week and that is based on LEAP data that isgenerated by the wearable electronic device and system of FIG. 1according to one or more embodiments shown or described herein;

FIG. 10E depicts a notional dashboard in the form of a daily geolocationchart that can be displayed on the remote computing device of FIG. 9 toallow a caregiver to determine recent history of patient outdoorspatio-temporal movement patterns based on LEAP data generated by thewearable electronic device and system of FIG. 1 according to one or moreembodiments shown or described herein;

FIG. 10F depicts a series of time markers taken during a pilot programin order to demonstrate pacing or agitation raw data that is generatedby the wearable electronic device and system of FIG. 1 according to oneor more embodiments shown or described herein;

FIG. 11A depicts a program structure for hierarchical relationships ofparticular HAR-related movements that may be determined based on LEAPdata generated by the wearable electronic device and system of FIG. 1according to one or more embodiments shown or described herein;

FIG. 11B depicts a program structure for a decision tree of certainHAR-related movements based on an accelerometer portion of the LEAP datagenerated by the wearable electronic device and system of FIG. 1according to one or more embodiments shown or described herein;

FIG. 12 depicts a data structure in the form of a dementia timelinechart to correlate the LEAP data gathered by the wearable electronicdevice and system of FIG. 1 to a change in functional status accordingto one or more embodiments shown or described herein;

FIG. 13 depicts a data structure in the form of an inventory chart tocorrelate the LEAP data gathered by the wearable electronic device andsystem of FIG. 1 to indicia of patient agitation according to one ormore embodiments shown or described herein;

FIG. 14A depicts a program structure in the form of a flow diagram ofhow the wearable electronic device and system of FIG. 1 may be used tohelp a caregiver determine if a patient is at risk of developing a UTI;

FIG. 14B depicts a program structure in the form of a flow diagram ofhow the wearable electronic device and system of FIG. 1 may be used tohelp a caregiver determine if a patient is at risk of developingneuropsychiatric complications;

FIG. 14C depicts a program structure in the form of a flow diagram ofhow the wearable electronic device and system of FIG. 1 may be used tohelp a caregiver with a medication algorithm once a determination ismade from a neuropsychiatric complication of FIG. 14B; and

FIG. 15 depicts a data structure in the form of a chronic diseasetrajectory timeline chart to correlate the LEAP data gathered by thewearable electronic device and system of FIG. 1 to a change infunctional status according to one or more embodiments shown ordescribed herein.

It will be appreciated that for the sake of clarity, elements depictedin the drawings are not necessarily to scale, and that certain elementsmay be omitted from some of the drawings. It will further be appreciatedthat certain reference numerals may be repeated in different figures toindicate corresponding or analogous elements.

DETAILED DESCRIPTION

The disclosed devices, systems and methods allow for real-time locationtracking, in addition to the utilization of a smart internet of things(IoT) configuration to provide data-informed care insights forindicators of potential health complications. Such an approach asdisclosed herein is particularly beneficial in medical or healthconditions that have traditionally been difficult to diagnose, such asAlzheimer's Disease and other forms of dementia. By utilizing a hybridwireless communication module, the wearable electronic device has theability to receive both a GNSS signal and a BLE signal, as well as theability to transmit an LPWAN signal (in general) and a Long Range WideArea Network (LoRaWAN™) signal (in particular) to provide data that maybe used by the system to not only provide indicia of a patient's currentlocation, but also through the recognition and analysis of one or moreof patient environmental, activity and physiological data (possibly inconjunction with the location data) to help a caregiver to identifychanges in one or more salient indicators of the health of that patient,including whether such changes may necessitate caregiver intervention.Moreover, having the wearable electronic device be affixed or otherwisesecured to a wearer allows for continuous monitoring, as well as theability to be scaled-up to allow the monitoring and analysis of largegroups of individual wearers. Furthermore, such an architecture permitssuch monitoring and analysis to take place in actual user environments,such as a home, assisted living community or the like. In addition, thetemporal nature of the collected data—coupled with using variousdata-gathering modalities for such data collection and otherdetermination—allows increased contextual insight into a wearer'smoment-to-moment activities, which in turn promotes greater accuracy inthe ability to analyze the health of the person wearing the device.Furthermore, the automated nature of the data being collected by thewearable electronic device alleviates human-error concerns associatedwith required user input of a particular event such as activity andevent recording, pressing a daily in-room “check-in” style button or thelike. As will be discussed in more detail later, this may beparticularly beneficial in avoiding the inclusion of erroneous data intoa machine learning or cloud analytics model where the presence of falsepositives or false negatives from such erroneous data may be both hardto identify and destructive of the accuracy of the data corresponding tothe person being monitored.

In addition to the wearable electronic device, the present disclosureteaches a system that may track and monitor the device, log thecollected data, perform analysis on the collected data and generatealerts. The data and alerts may be accessed through a user interfacethat can be displayed on a remote computing device or other suitabledevice with internet or cellular access. Another component of the systemmay be that a specified predetermined area in which the patient can movefreely can be set by placing reference point or sentry beacons; in oneform, such predetermined area may be set up as a geofence or the like sothat if the patient wanders outside the predetermined area, a signal issent to the central processing unit, which then generates a signal oralarm to notify one or more of the patient, family members, medicalprofessions and other caregivers.

I. Configuration Overview

Referring first to FIG. 1, an overview of the patient monitoring system1 architecture is shown. In one form, the system 1 includes a wearableelectronic device 100, one or more BLE beacons 200, a gateway 300 andbackhaul server 400 the last of which may function as a carrier-gradedata network. In another form, the wearable electronic device 100 may beoperated at least partially independent of the system 1 in order toacquire—among other types of data—location data from a GNSS 10 and theBLE beacons 200. Within the present disclosure, GNSS is the standardgeneric term for satellite navigation systems that provide autonomousgeo-spatial positioning with global coverage and is meant to includesspecific embodiments such as global positioning system (GPS), theRussian global navigation satellite system (GLONASS), Galileo, Beidouand other regional systems. In this latter form, at least some of thedata acquired by the wearable electronic device 100 may be operated uponlocally by the wearable electronic device 100 itself, while otheroperations (such as those requiring larger amount of processingcapability) may be performed remotely within the backhaul server 400 orother component within system 1. Either of these forms—as well as ahybrid of both—are deemed to be within the scope of the presentdisclosure. As such, some or all of the data being acquired, as well asoperations performed on such data for the purpose of tracking thelocation of or analyzing the health condition of an individualassociated with the wearable electronic device 100 may be optimizeddepending on the need. Within the present disclosure, an individual,patient or other person is deemed to be “associated with” the wearableelectronic device 100 when such device is worn, affixed or otherwisecoupled to the individual such that data acquired from such individualor pertaining to such individual's immediate environment may be used tohelp ascertain or determine one or more of location and health conditioninformation for such individual. Additionally within the presentdisclosure, the terms “location data”, “location information” and theirvariants are meant to encompass information that establishes (eitherdirectly or through additional calculations) absolute or relativelocation or position of the wearable electronic device 100 based onsignals it receives from RSSI or GNSS sources such as those discussedherein. Thus, for example, the data contained within an emitted RSSIsignal may be translated into a relative location between the source ofsuch signal and the wearable electronic device 100, in which case suchemitted RSSI data is deemed to be location data.

The flexible deployment of the wearable electronic device 100 and itswireless connectivity to various other components within system 1 may inone form enable edge computing (also referred to as fog computing, ineither event, where the wearable electronic device 100 functions as anedge device where some or all of the sensors 121 are deployed) thatwould allow the wearable electronic device 100 to function as an IoTdevice as part of a low-cost, highly-adaptable private network,especially allowing increased interaction with a cloud 500 or relateddata acquisition or analysis equipment through the gateway 300. In oneform, single chip packages or chipsets may be used to provide edgecomputing capability; an example of such an approach may be found inMicrosoft's Azure Sphere or Amazon's AWS. The modular nature of thewearable electronic device 100 allows it to function as an IoT devicewith hardware and software features to manage the flow of LEAP data, aswell as provide—or at least help to provide—analytics of the acquiredLEAP data. In one form of such edge computing, the wearable electronicdevice 100 may carry out a significant portion of the processing,storage and communication functions at a local level. In another form,the gateway 300 may possess at least some of the logic capability (suchas through its own logic device 173 and associated circuitry andinfrastructure) to be able to make decisions about the LEAP data, aswell as send alerts or data to other equipment either within system 1 orbeyond. In one form, the gateway 300 could also act as a mini server andmake all data decisions on its own to then send the data to externaldevices such as a phone or web browser, thereby forming another type ofedge computing configuration. Additional improvements in data andconnection security may also be realized with such edge-styleinteroperability, including the added security of having systemredundancy in situations where the internet or cloud 500 is inoperable.Such edge computing is beneficial in that can leverage the increasedcomputational and storage capabilities that are associated with cloudcomputing to the physical location of the wearable electronic device100, irrespective of its location within the network. Such aconfiguration in turn helps promote reduced network latency through acombination of real-time computing and localized resource pooling, aswell as providing increases in user data security with on-device storageof at least certain portions of the acquired data. Significantly,because the size of the data packages being sent from the wearableelectronic device 100 to the gateway 300, backhaul server 400 and cloud500 is relatively small, communication-based bandwidth problems may beavoided.

In situations where more than one of the BLE beacons 200 are used in agiven patient environment, such BLE beacons 200 may exhibit one or bothof structural and functional differences in the form of room beacons200A, elopement beacons 200B and nurse ID beacons 200C. For example, theroom beacon (also referred to as a reference point beacon or navigationbeacon) 200A is the first type of BLE beacon 200 and may be used in atransmit-only mode of operation to broadcast a unique identificationthat can be correlated to a particular fixed location (such as aparticular room within a multi-room dwelling) to the wearable electronicdevice 100. The second type of BLE beacon 200 is the elopement beacon(also referred to as a sentry beacon) 200B that may also be used in atransmit-only mode of operation to broadcast that a person equipped withthe wearable electronic device 100 is attempting to leave a permissiblezone (such as by exiting an external door) in a manner relativelysimilar to an RFID device. The third type of BLE beacon 200 is the nurseID beacon (also referred to as a personnel beacon, such as in the formof a card beacon or the like) 200C that is used to transmit to theserver 400 through the gateway 300 that a particular caregiver who isuniquely identified with each nurse ID beacon 200C is in a position toeither respond to a particular patient request, measure touchpointsbetween patient visits (as an indicator of nurse/patient interactions)or has complied with routine patient visits commensurate with of his orher job description or related employee obligations. In one form, thenurse ID beacon 200C may be integrated into a mobile telephone to takeadvantage of the telephone's Bluetooth or other communication capabilityso that in such form the nurse ID beacon 200C might also possess (in amanner unlike the both the room beacons 200A and elopement beacons 200B)both transmit and receive capabilities.

In one form, some of these types of beacons 200 that differ based ontheir function may still exhibit some similarity in their structure. Forexample, the room beacon 200A and the elopement beacon 200B may be madefrom the same hardware or structure (including battery, radio andprocessor), while differences in their function may be based onadjustments made to certain operational attributes such as the scan rateor power output (which in turn helps the wearable electronic device 100determine which beacon is closer). Likewise, the nurse ID beacon 200C—inaddition to possibly having a different form factor—may have its scanrate or power preset to different (for example, lower) levels. In oneform, the nurse ID beacon 200C may have a 100 millisecond scan rate anda power level of −20 dBm, thereby ensuring that the nurse or othercaregiver associated with a particular nurse ID beacon 200C isphysically close to the patient that sent the nurse call request. Thisin turn increases the likelihood that the nurse or caregiver actuallyresponded to the request for assistance.

In one form for a location where multiple patients may be equipped withthe wearable electronic device 100, the transmission between the variousBLE beacons 200 to the various wearable electronic devices 100 may bearranged in a mesh topology network that differs from the star topologyof the transmission that takes place from the various wearableelectronic devices 100 to the one or more gateways 300. In addition,various form factors for BLE beacon 200 may be used. In one form, theBLE beacon 200 may be lithium-chip battery powered, such as thatassociated with the disk-shaped variant, the coin variant and the creditcard-shaped variant. In another form, the BLE beacon 200 may take itspower from an alternating-current source such as that of the walloutlet-mounted or computer expansion slot-mounted variants either ofwhich may include a high definition multimedia interface (HDMI) oruniversal serial bus (USB) slot or mount. Irrespective of the shape orpower source, BLE beacon 200 is configured to transmit an RF signal atvarious frequencies such as 915 MHz (US), 868 MHz (Europe) and 430 MHz(Asia). In one form, the BLE beacon 200 could be configured as the coinbeacon such as that embodied by the BlueCats BC413, while the gateway300 can be a BlueCats edge relay, both manufactured by BlueCats.com ofAustin, Tex. In one form, the BLE beacon 200 may be run entirely withina private network, while in another, it may be made to communicatesecurely with the cloud 500 or a related high-performance computing(HPC) equipment.

The previously-discussed transmit-only functionality of the room beacons200A and the elopement beacons 200B in their communication with the oneor more of the various wearable electronic devices 100 promoteslower-cost and less complicated installation and maintenance than insituations where two-way communication between the wearable electronicdevices 100 and the BLE beacons 200 may be present. Such an arrangementalso helps to avoid communication ambiguity between the wearableelectronic devices 100 and these two forms of the BLE beacons 200.

In one form, the nurse ID beacon 200C may be used to track theactivities of the remote computing device 900 that is associated with aparticular nurse or related caregiver in response to a data transmissionfrom one or more of the wearable electronic device 100 and system 1. Oneexample of the nurse ID beacon 200C is in a card-shaped format such asthat manufactured by Kontakt.io, Inc. of New York, N.Y. In this form,the nurse ID beacon 200C may be wearable or otherwise affixable to thecaregiver (such as employees of an assisted living community) throughhis or her corresponding remote computing device 900. As such, onefunctional difference between the room and elopement beacons 200A and200B and the nurse ID beacon 200C resides with the nurse ID beacon 200Cadditionally having a limited receive capability in order to receive analert, warning or other important message from a separate source such asa remote computing device 900 (that will be discussed in more detail inconjunction with FIGS. 4 and 5) or related call center, monitoringstation or the like. For example, should an individual press a nursecall button 131 (also referred to as a help button, and that will bediscussed in more detail in conjunction with FIGS. 2A and 2H) that islocated on the wearable electronic device 100, the correspondingdistress or related request for assistance is sent from the wearableelectronic device 100, through the gateway 300 and one or more of thebackhaul server 400 and cloud 500 and to patient-monitoring equipmentsuch as the remote computing device 900 in order to then have a messagesent (for example, over a wireless internet, WiFi, cellular or othersuitable connection) to the various nurse ID beacons 200C so that onethat is in closest proximity or readily-available to lend assistance maydo so. Another functional difference among the various types of beacons200A, 200B and 200C may be in their transmit power, identifier or thelike. It will be appreciated that these and other similarities anddifferences may be adjusted in order to configure the BLE beacon 200 fora particular application, and that all such variants are deemed to bewithin the scope of the present disclosure. For example, the room beacon200A may be arranged to cover a relatively large area such as a room,and as such may transmit at a power sufficient to cover the entirety ofsuch room as part of an RSSI or other distance-based finding approach.In this type of operation, the room beacon 200A sends an identifier suchas a universally unique identifier (UUID) or similar data package, sothat the wearable electronic device 100 may measure RSSI and use thismeasurement to assist with location determination of the relativeposition between them. Likewise, the elopement beacon 200B and thewearable electronic device 100 cooperate in a nearer-field manner toemulate a radio frequency identification (RFID) arrangement such thatshould a person wearing the electronic device 100 pass from a permittedspace and through door or other point of egress where the elopementbeacon 200B is situated, the identifier being sent from the elopementbeacon 200B is received by the wearable device 100 that in turn cantransmit a signal that indicates such patient proximity to the gateway300 and server 400 the latter of which is equipped with internet,cellular or other backhaul-connected modes of communication to notifysuitably-configured electronic devices (such as remote computing devices900). Similarly, the nurse ID beacon 200C may have an even lower powersetting than the elopement beacon 200B such that upon touch activationof the nurse call button 131 (that will be discussed in more detail inconjunction with FIG. 2H) on the wearable electronic device 100, aportion of a hybrid wireless communication module 175 (that will bediscussed in more detail in conjunction with FIG. 2F) on the wearableelectronic device 100 is activated in order to act like a tracker thatreceives the identifier being sent from the one or more nurse ID beacons200C that may be in the vicinity. In this way, a particular one of thenurse ID beacons 200C that is identified as having the strongesttransmission signal (and therefore deemed to be in close proximity tothe patient) can in turn receive a corresponding signal from the server400 to instruct the caregiver to attend to the individual who activatedthe nurse call button 131. Because the identifier being transmitted fromthe nurse ID beacons 200C can be coded to also include identification ofa particular caregiver, better control over caregiver actions,whereabouts, patient response times or the like can be established. Forexample, activation of the nurse call button 131 may also triggeranother portion of the hybrid wireless communication module 175 to sendan LPWAN-based signal to a data repository that may be maintained on thebackhaul server 400, cloud 500 or elsewhere to particularly identify theresponsible caregiver and his or her subsequent activity.

BLE beacon 200 broadcasts a radio signal through its RF connection suchthat the contents of its data package is made up of a combination ofletters and numbers transmitted on a regular interval. As will beappreciated, the UUID or related identifier information iscontext-specific in that its meaning is dependent upon anotherapplication (such as that running on the server 400 or another externaldevice or other program in order to recognize the meaning of the datathat corresponds to the identifier information). In such circumstance,the wearable electronic device 100 merely has to read the UUID broadcastfrom the BLE beacon 200 and send the information to the external deviceapplication such that the program performs the remaininglocation-determining calculations. When a dedicated external deviceapplication recognizes the wearable electronic device 100 as a nearbyBLE-enabled device, the application links the device to an action orpiece of content (such as that which may be stored in the cloud 500) anddisplays it to a local or suitably-connected remote user. In one form,an online-based approach lets a user manage, configure and update BLEbeacons 200 and their profiles. For example, a display-based dashboardmay be visible on a web page that is signally coupled to a patientdatabase that permits as-needed updating. Because the BLE beacon 200 isconfigured to transmit its signals over relatively short distances (forexample, tens of meters or less), its hardware may be of simple,low-cost construction, often including only a CPU, RF radio and abattery; such a lower per-unit cost may become an important factor intotal infrastructure costs, particularly where large numbers of BLEbeacons 200 may be installed in a single facility or group offacilities. Significantly, the low current draw associated with the BLEformat allows the batteries that are used to power the BLE beacons 200to consume far less energy than conventional Bluetooth-based deviceswhile maintaining a comparable (or even greater) communication range.

Various communications standards (also referred to as pseudo-standardsoftware protocols, such as iBeacon, Eddystone or AltBeacon) may be usedto determine the BLE beacon 200 transmission characteristics andinteraction with other wireless devices for proximity awareness. Amongother things, the communication standard controls the data format andcontent of the advertising packet payload that is structured to have oneor more of the UUID or related identifier, a media access control (MAC)address, major and minor fields, manufacturer identification or thelike. While each standard has some individually-definingcharacteristics, including the nature and size of the advertising packetthat makes up the location-based signals being sent to the wearableelectronic device 100, they all follow a relatively uniform data formatto allow the operating system of the wearable electronic device 100 toadapt, regardless of how such location information is being sent fromthe beacon 200. For example, if the BLE beacon 200 is transmitting usingthe iBeacon standard, the advertising data packet format includes 30bytes of total payload of which the UUID proximity is 16 bytes, a 1 bytepreamble, a 2 byte major, a 2 byte minor and a 1 byte transmit power.Regardless of which standard is used, when the wearable electronicdevice 100 is within the radio range of at least one room beacon 200A,the wearable electronic device 100 receives the UUID or relatedidentifier to allow both identification and RSSI-based information. Inone form, when the UUID and its associated signal strength are passed tothe cloud 500 through the wearable electronic device 100, gateway 300and the backhaul server 400, the cloud 500 is able to determine a groundtruth through mapping the UUID of a corresponding one of the BLE beacons200 to a physical location. The one or more room beacons 200A each sendout their respective UUIDs at regular intervals (such as about ten timesevery second, although depending on the settings, such frequency can beincreased or decreased). In addition to using the UUID or relatedidentifier as a way to acquire relative location between the BLE beacon200 and the wearable electronic device 100, when a dedicated applicationthat has been set up on the wearable electronic device 100 as well asother remote computing devices 900 of FIGS. 4 and 5 recognizes the UUID,it links the location-based information from the RSSI signal to thebackhaul server 400, cloud 500 or the like that in turn can be sent(such as over the internet) for display to a caregiver, family member orother interested party that has a suitably-equipped application on theirown remote computing device 900. One exemplary form of the room beacon200A type of BLE beacon 200 is the coin beacon Model BC413 mentionedpreviously; this beacon employs a stand-alone profile with extended (forexample, 1 year) battery life, Bluetooth 4.0+ specification operating inthe 2.402 GHz to 2.480 GHz range with normal operation range of about100 meters.

One notable feature is that in the namespace ID (which may be part of apayload data unit (PDU)), the wearable electronic device 100 not onlyacquires beacon advertisements or related signal broadcasts, but is alsoable to ascertain what type of BLE beacon 200 is transmitting thesignal. Based on that information, the wearable electronic device 100can respond differently. For example, if it sees a nurse ID beacon 200C,it will know to turn off the nurse call button 131. Likewise, if it seesan elopement beacon 200B it will send the message through a thirdwireless communication sub-module 175C (that is part of the hybridwireless communication module 175 of FIG. 2F) faster than a normal roombeacon 200A could. With this, in the nurse ID beacon 200C for example,(1) the fact that it is in response to the nurse call button 131 on thewearable electronic device 100 having been pushed can be known, and (2)which nurse the nurse ID beacon 200C belongs to, for example, that 01equates to a nurse call, and that 12 equates to a particular nurse (suchas “nurse Betty”). Significantly, this enables the wearable electronicdevice 100 to act as a dynamic instrument rather than merely as staticread and relay device where the nurse call beacon could be representedin the 16-bit UUID format with a certain number, for example: 01 12 0000 00 00 00 00.

While conventional WiFi or Bluetooth (such as Bluetooth Classic andother non-BLE versions) may be used to transmit or receivelocation-based signals with the wearable electronic device 100, theauthors of the present disclosure have discovered that reliability andcost concerns favor the use of BLE beacons 200 for sending indoorlocation-detecting signal transmissions to the wearable electronicdevice 100, particularly when the assisted living community, hospital,home or other dwelling or place of recovery is of older constructionwhere one or more of building layout, choice of construction materialsor other factors can degrade a WiFi signal or Bluetooth signal relativeto a BLE signal. In one form, the transmitted signals from the BLEbeacons 200 can be received by a nearby BLE radio (for example, in theform of a chip that cooperates with or makes up a part of a firstwireless communication sub-module 175A that will be discussed in moredetail in conjunction with FIG. 2F). Furthermore, the authors of thepresent disclosure have discovered that providing both indoor andoutdoor location-detection capability is particularly beneficial insituations where the person that is secured to the wearable electronicdevice 100 is in danger of wandering (such as due to the various formsof cognitive deficit, impairment or related frailties discussed herein).In this regard, the short range of conventional WiFi, RFID or other“indoor-only” approaches is prone to losing contact with a wander-proneindividual during periods where the individual gets beyond such rangeand is most vulnerable to harm, such as during adverse weatherconditions, or while in a dangerous topographic, high-vehicular trafficor high-crime area. Relatedly, GNSS or a related “outdoor-only” approachis incapable of tracking such individuals indoors where the overwhelmingmajority of their daily activities are spent, as many of theseindividuals are already residing either at home or in an assisted-livingor other specialty-care facility where access to outdoor endeavors issignificantly limited.

Within the present context, the term “LPWAN” will be understood toinclude low power, low data bit-rate wireless communications that arecapable of sending a transmission over long ranges in general, wherevarious proprietary and open-system protocols (for example, Sigfox,Ingenu and LoRa the last of which forms a part of thepreviously-discussed LoRaWAN™ under the guidance of the LoRa Alliance)may be used as specific implementations, for example, as a MAC layerprotocol for controlling the communication of LPWAN gateway 300 and thewearable electronic device 100. As such, both the general (for exampleLPWAN) and the specific (for example, LoRaWAN™ or the like, with its useof the LoRa as the physical layer with its chirp spread spectrum (CSS)modulation) are understood within the present context to be the vehiclethrough which one or more components of the collected LEAP data arecommunicated from the wearable electronic device 100 to the system 1through gateway 300, and that the understanding of the specific or thegeneral will be apparent from the context. Moreover, the authors of thepresent disclosure have discovered that using LPWAN (including itsLoRaWAN™-specific variant) is preferable for the transmission ofcollected data from the wearable electronic device 100 to the gateway300. In one form, the LPWAN signal used to convey data collected by thewearable electronic device 100 is predominantly used in a one-way flowof such information in an uplink manner to the gateway 300. Consistentwith any of Classes A, B or C communication and an attendantbidirectional communication capability, some form of downlink may alsobe employed in order to establish security updates, data transmission(i.e., received packet) acknowledgement, other over-the-air updates orthe like. In such downlink communication, an application server 420 thatis part of the backhaul server 400 may communicate with a network server410 that is also part of the backhaul server 400 and that in turn sendseach downlink message to a single gateway 300 that then transmits themessage to the wearable electronic device 100, such as through an LPWANsignal to the third wireless communication sub-module 175C.

As mentioned previously, distances may be measured with RSSI-basedmethods such as to acquire indoor localization. Such a simplifiedapproach permits a relatively accurate range and distance calculationwithout having to rely on the infrastructural complexities such asscannable or multiple antennas associated with time of arrival (TOA),time difference of arrival (TDOA, such as multilateration, AOA ortriangulation) and related angle measurement approaches, among others.Rather than rely upon relative time measurements, synchronized clocks orthe like, RSSI employs a simple measured signal strength approach thatcan achieve a desired level of location accuracy without the complexityor power-consuming attributes associated with other systems. Inaddition, RSSI used in localization activities may be coupled to eitherdeterministic or probabilistic algorithms that allow the signal beingbroadcast to be translated into distances from BLE beacon 200 points bymeans of theoretical or empirical radio propagation models. For example,a deterministic model stores scalar values of averaged RSSImeasurements, while a probabilistic model selects a location from aknown RF map with the highest likelihood of being correct. In one form,the RSSI can use the following expression to account for a general radiopropagation model.

$P_{r} = {{P_{t}\left( \frac{\lambda}{4\pi\; d} \right)}^{n}G_{t}G_{r}}$

In it, the received power is Pr, while the transmitted power is Pt, Athe wavelength of the radio signal, Gt and Gr the gains of therespective transmitter and receiver antennas 140, d the distanceseparating the two antennas 140, and n is the path loss coefficient,typically ranging from 2 to 6 depending on the environment. By usingRSSI, the BLE beacons 200 and associated wearable electronic device 100avoid the need for—as well as cost associated with—multiple antennas ora scannable antenna. Likewise, by removing the need for engaging incostly training and complex matching algorithms, the architecture of thepresent system 1 is simplified. One approach that may be used toestimate the location of the wearable electronic device 100 through RSSIinvolves so-called fingerprinting, where a pre-recorded ground truth mapof the area of interest is created to infer locations through abest-matching method. Such an approach is relatively immune to the typesof diffraction, reflection, multipath and other non line-of-sightconditions, interferences and other shadowing effects that are prevalentin multi-unit dwellings such as assisted living communities, as well ashomes where there may be clear lines of demarcations between variousrooms therein. In one form, RSSI may be combined with other BLE-basedattributes such as adaptive frequency hopping to allow the transmissionof data over a large number (for example, 40) channels with physicallayer options that support data rates from 125 kbps to 2 Mbps, as wellas multiple power levels from 1 mW to 100 mW to promote clear,interference-resistant signals. The previously-mentioned AOD approachmay be used, depending on the antenna configuration of the signaltransmitting and receiving devices. In one form, these AOA and AODapproaches may use the emerging Bluetooth 5 and its IoT-specific mode ofBLE operation.

Also as mentioned previously, in situations where even greater locationaccuracy may be required (such as an absolute value of the location ofthe wearable electronic device 100), hyperlocation-based approaches maybe used, including those that augment or replace RSSI with some form ofAOA or AOD that are equipped with adaptive digital beamformingalgorithms in conjunction with multiple-antenna arrays. The beamformingalgorithms may be based on a subspace-based estimator such as multiplesignal classification (MUSIC), Bartlett's method, correlativeinterferometry, the Welch method, the Capon algorithm or the like thatcan measure the location of the wearable electronic device 100 forsituations where the AOA or AOD is detected by an array of at least twoantennas positioned with a known geometry. In another form differentthan that of AOA or AOD, TOA, TDOA and related time-of-flight (TOF)approaches may be used. Such forms may employ a two-step TDOA approachto measure travel time and trilateration using (i) time delay estimationand (ii) sound source localization to measure the time delays betweenthe signals coming from each of various sensors 121. It will beappreciated that such an approach will introduce a more comprehensivelevel of setup, equipment and related infrastructure than the purelyRSSI-based approach and as such may be contingent upon infrastructureand related budgetary constraints. In one form, augmented antenna arrayscorresponding to AOA communications may be included in or on a housing110 and support tray 120 in a manner generally similar to antenna 140.In another form, location tracking for use with the BLE beacons 200 mayalso include triangulation positioning, fingerprint and Kalman filteringalgorithm-based approaches.

By using an LPWAN-based communication protocol to transmit data from thewearable electronic device 100 to the gateway 300, overall system 1architecture and operability is significantly improved. In particular,such a protocol and its low bit-rate data transmission avoids thenecessity to connect the wearable electronic device 100 over a cellular,GMS, LTE or related high-bandwidth network. In addition to reducing usercost by not requiring a cellular contract or data plan, the LPWAN-basedcommunication protocol also simplifies system installation in that itsstandalone structure does not require complex information technology(IT) infrastructure installation, integration or upgrading. Furthermore,adopting an LPWAN-based approach avoids causing the end-use devices(that is to say, the wearable electronic device 100) to become obsoleteor otherwise incompatible with the remainder of the system 1 in themanner of cellular-based protocols (such as the progression from 2G to3G to 4G, as well as the imminent 5G). The LPWAN network architectureimproves operability relative to cellular-based networks and theirreliance upon smartphones or related high-power, high-bandwidth userdevices for the transmission of relatively smaller size data packets. Assuch, a significant portion of the LPWAN's computing capability may bemoved to other places within the system 1, as well as to externalequipment, such as the cloud 500. As such, the LPWAN-based mode ofcommunication, coupled with the compact multimodal sensing provided bythe wearable electronic device 100, is capable of virtualizing numerousnetwork node functions into a modular, reconfigurable approach toestablishing device communication with downstream caregiver and analyticresources without the need for custom hardware and associatedconfiguration requirements for each network function. LPWAN operationmay fall under one or more standards, including IEEE 802.15.4k,802.15.4g, 802.11 long range low power, IETF 6LPWA/LPWAN, LoRaWAN™,Weightless SIG, Dash? Alliance or the like, depending on whether certainproprietary or open-source chipsets are employed. In one form, theLoRaWAN™-based approach is used for the LPWAN signal transmission wheredata rates of between 20 kbps and 50 kbps (with a particular data rateof about 35 kbps, in one form) are possible over transmission ranges ofbetween roughly 1 mile and 10 miles (depending on the line-of-sightattributes of the ambient environment), with multiple uplink anddownlink channels over a star-based gateway topology and 128 bit dataencryption.

The gateway 300 is a data-bearing router-based intermediary between thelow power network packet-based transmissions of the wearable electronicdevice 100 and the high bandwidth networks (such as internet protocol,cellular or WiFi) associated with the cloud 500. It acts as aduplicating, packet-forwarding device by first receiving LPWAN radiosignals from events recorded and stored in memory 173B (that will bediscussed in more detail in conjunction with FIGS. 2F and 2G) of thewearable electronic device 100. The gateway 300 takes these events andforwards them to the backhaul server 400. In one form, the gateway 300may be operated with a full operating system such that the software usedfor packet-forwarding is operating in the background while othersoftware may be adjusted as needed, while in another form operatedalmost exclusively with dedicated packet-forwarding software. Inaddition, in one form, communication between the wearable electronicdevice 100 and the gateway 300 may be configured such that up to sixdifferent LoRaWAN™ network credentials may be stored to allow hoppingbetween credentials seamlessly if one isn't available or otherwise losesconnectivity. Such functionality may also work in situations when aprivate network between these components is being employed (such as fora nursing home, assisted living facility or the like) to then allowas-needed switching to a public network (such as that provided byinternet service providers (ISPs) for example). In one form, eachgateway 300 can serve numerous (for example, in excess of a thousand ormore) wearable electronic devices 100. Having multiple gateways 300 maybe helpful in establishing a star topology for a network formed betweensuch gateways 300 and the one or more of the wearable electronic devices100. In addition to that manufactured by the previously-mentionedBlueCats, other examples of gateway 300 include those manufactured byLink Labs, Multitech and Laird, among others. In one form, gateway 300may be battery powered, while in another, it may be powered by aconventional alternating current (AC) outlet. When configured as part ofthe previously-discussed edge computing capability, the gateway 300 mayfurther be configured to have redundancy in the event of a power outage.For example, should the gateway 300 lose its electrical power supply, itcan convert from using an ethernet-based backhaul to a cellular-poweredone.

The backhaul server 400 is coupled to cloud 500 and is shown presentlyin the form of the network server 410 and the application server 420,although it will be appreciated that in an alternative form, the twoservers may be subsumed under a single server, and that functionsassociated with the backhaul server 400 may be combined with othercomputers or servers (not shown) on an as-needed basis. Likewise, withinthe present context, in some embodiments, the system 1 may exclude oneor more of the components disclosed herein, while in some embodiments itmay add another component to those presently disclosed, and that it willbe appreciated that these and other variants are all deemed to be withinthe scope of the system 1 of the present disclosure. In the presentcontext, the term “backhaul”, “backhaul server” and its variantsrepresents the portion of system 1 that provides the intermediate linksbetween the internet or associated network and wearable electronicdevices 100 that define the data-gathering (including sensor-based datagathering) that takes place at the so-called network edge. Unlike theprivate-network infrastructure associated with the connection betweenthe wearable electronic device 100, the BLE beacons 200 and gateway 300,backhaul communication between the servers 400 of the system 1 and theinternet may take place by public-network infrastructure, includingtelephone companies or ISPs, as well as through a public broadbandbackhaul. In one form, a portion of server 400 may function as a cloudserver (also referred to as a virtual server or virtual private server)over the internet as part of an infrastructure as a service (IaaS) basedcloud service model. In one form, the system 1 and its servers 400 alongwith suitably-connected databases contained therein or on the cloud 500may engage in data exchange through standardized protocols such as thoseassociated with public-private key exchanges, hypertext transferprotocol (HTTP), secure HTTP (HTTPS), advanced encryption standard(AES), web service or native application programming interfaces (APIs,that is to say, “apps”) or other electronic information exchangeapproaches. Similarly, in one form, the exchange of data between thesystem 1, servers 400 and cloud 500 may take place over the internet,VPN, a packet-switched network, local area network (LAN), wide areanetwork (WAN) or related packet-switched network.

The network server 410 functions to provide hardware and peripheralequipment support access, disk space for file storage or the like toother computers in the system 1, as well as handle software,configuration or security updates. In one form, the network server 410acts as the interface between the wearable electronic device 100 and theapplication server 420 in a manner defined by the LoRa Alliance toeliminate duplicate packets, schedule acknowledgements, adapt datarates, and provide encrypted communication. In one form, the networkserver 410 acts as a database server, and may also include checking andverifying device identification functions for the wearable electronicdevice 100, performing message integrity code (MIC) or MAC checks aswell as serving as a duplicate filter (all for data uplinks). Thenetwork server 410 then proceeds to send the messages to the applicationserver 420. The network server 410 is used in order for an LPWAN radiosuch as that embodied in the third wireless communication sub-module175C of the wearable electronic device 100 to communicate with theapplication server 420. For example, when the LPWAN signal istransmitted, the network server 410 eliminates duplicate packets,schedules acknowledgements, adapts data rates and provides encryptedcommunication with the wearable electronic device 100, as well as otherdevices. Such strong encryption, device and user authentication methods,as well as filtering of incoming signals, can help to secure thewearable electronic device 100 from unauthorized access, as well asprovide enhanced intrusion detection. In one form (not shown) afirewall, bastion host or other security component may be used toestablish a security perimeter to help isolate, segment and control datatraffic flows between the wearable electronic device 100 and theremainder of the system 1.

The application server 420 is part of the cloud 500 and serves as acomputing nerve center for system 1 to run protocols and interfaces,such as web-based protocols and the previously-mentioned APIs. Functionsprovided by the application server 420 include (i) reception ofLoRa-based messages from the network server 410, including the decodingof messages or packets, (ii) archival of events such as location, nursecalls, battery level or the like that are sent from the wearableelectronic device 100, (iii) configuration data such as identificationof users, devices, beacon location, audio files or the like, (iv) rulesfor sending notifications to caregivers and family applications, (v)security and (vi) storage of new firmware versions for the wearableelectronic device 100. In one form, the application server 420 iscoupled with a web server (that in one form is subsumed under networkserver 410) that may be coupled to the cloud 500, while in another formit is an integral part of such a web server to be referred to as a webapplication server that can during data uplink act as a medium for(among other things) data storage, visualization and message uplinktriggers. For example, the application server 420 may be configured in amanner similar to Amazon Web Services (AWS), and as an IoT device or thelike to provide cloud-based infrastructure, where in one form variouscode, logic or the like may be developed and implemented. For particulardownlink operations, the application server 420 may also perform packetencryption and packet queueing functions. In one form, data streamingmay be set up by creating notification targets for the wearableelectronic devices 100 so that the notification target configures thenetwork server 410 to stream wearable electronic device 100 uplinks to auser-specified destination such as the remote computing devices 900.Such data streaming may be used in situations where the need forreal-time analytics based on the acquired LEAP data through the wearableelectronic devices 100 is of time-sensitive.

Within the present context, the cloud 500 (and cloud computing) isunderstood to be the delivery of services over the internet, as well asthe hosting of such services. In this way, the server 400 is separatedfrom the service being provided. This allows for simplification of theend user equipment (in this case, the wearable electronic device 100 andgateways 300) such that in one form, much or all of the data and models(such as machine learning or cloud analytics models as discussedelsewhere in this disclosure) may be hosted on a server in the cloud500, allowing the end user to access it through the internet. In oneform, the application server 420 is a cloud-based computing device thatserves as a so-called “nerve center” for the system 1, and may be loadedwith software that provides instructions for operation of the system 1,including (i) the reception of messages from the network server 410,(ii) the archival of events (for example, acquired LEAP data, system orcomponent status or operability such as remaining life of a battery 180(that will be discussed in more detail in conjunction with FIG. 2F) thatis used to provide electric power to the wearable electronic device 100,time-stamping, alerts or notifications, or the like) sent from thewearable electronic device 100, (iii) configuration data (such as users,devices, beacon location, audio files or the like), (iv) rules forsending notifications to caregivers and family applications, (v)security and (vi) storage of new firmware versions for the wearableelectronic device 100. As with the application server 420, thecloud-based approach may leverage known scalable virtual machines, suchas AWS EC2, AWS Elastic Beanstalk, AWS Lambda or the like.

In one form, ancillary equipment may include an inductive-chargingdevice 600 for periodic reenergizing of the wearable electronic device100 and its battery 180. The inductive-charging device 600 may be basedon the Qi open interface standard for low power inductive transfer. Inone form, the inductive-charging device 600 may receive its powerthrough a conventional AC electrical outlet where, for example, thirtyminutes can provide enough charge to the battery 180 to have it lastabout three days. In addition, a WiFi router 700 may be included totransport audio to a standalone WiFi speaker 800 that may be used inaddition to or in place of a speaker included in the wearable electronicdevice 100. The WiFi router 700 is either provided by the care facilityor can be part of the system 1 installation, whether at home, a carefacility or elsewhere where patient monitoring may be needed.

In one form, the conceptual model on how at least a portion of thesystem 1 and the wearable electronic device 100 are arranged tostandardize their various communication functions, especially whencoupled to an external network such as the cloud 500, as well as betweenthe servers 400 and the WiFi router 700 and WiFi speaker 800, followsthe Open Systems Interconnection (OSI) hierarchical communication model.This model employs a communication protocol stack that partitions acommunications into seven abstraction layers that specify how datashould be packetized, addressed, transmitted, routed and received; theseare the application, presentation, session, transport, network, datalink and physical layers. As with the previously-discussed LPWAN-basedmode of communication and its associated virtualization of numerousnetwork node functions into a modular, reconfigurable way to establishcommunication between devices, organization into the various layersunder this model is beneficial in that it allows system 1 functionalityto be described without regard to the underlying physical structure ofthe various components, which in turn promotes an increased amount ofcomponent or computing device independence. It will be appreciated thatmultiple possible abstraction layer configurations are possible in thedeployment of a network used to support the operation of the system 1disclosed herein, and that the use of the unique architecture of thewearable electronic device 100 in general and the particular arrangementof the hybrid wireless communication module 175 disclosed herein willassist in improvements in overall operability of the system 1.

Within the seven layers of the OSI model, the lowest three are oftengrouped together as media layers, while the upper four are often groupedtogether as host layers. The physical layer is the fundamental layerwithin the media layer group, and supports the logical data structuresof the higher level functions of other layers, and includes thenetworking hardware transmission technologies responsible fortransmitting raw bits of information rather than logical data packets,and provides an electrical, mechanical and procedural interface to theradio frequency (RF) transmission medium. With particular regard to thesystem 1 when configured as an RTLS, the physical layer is embodied asthe wireless RF communications discussed herein. In such case, the BLEbeacons 200 may act as tags and (at least in the room beacon 200A types)fixed reference points. The data link layer is the protocol layer thattransfers data between adjacent network nodes in a WAN or between nodeson the same LAN. The data link layer may also include error-correctionfeatures. In one form, the combination of the data link layer and thephysical layer of the OSI protocol stack may be thought of as thefunctional equivalent of the so-called link layer of the transmissioncontrol protocol/internet protocol (TCP/IP) model. The data link layerincludes a MAC sublayer that makes it possible for several network nodes(such as the LPWAN gateways 300 and wearable electronic devices 100 ofthe disclosed system 1) to communicate over a shared medium. In oneform, LPWAN may use an ALOHA-style scheme in the MAC layer that incombination with the physical layer enables multiple wearable electronicdevices 100 to communicate at the same time but using one or both ofdifferent channels and orthogonal codes (i.e., spreading factors). Inthe LoRaWAN™-specific version discussed elsewhere within the presentdisclosure, the MAC layer protocol for managing communication betweenthe gateway 300 and the end-node wearable electronic device 100 ismaintained by the LoRa Alliance. The network layer responds to servicerequests from the transport layer (discussed below) and issues servicerequests to the data link layer in order to provide routing and datapacket-forwarding from a source to a destination through one or morerouters. As will be discussed in more detail below in conjunction withFIGS. 3A through 3I, a data packet includes control information as wellas user-specific payload. The transport layer is responsible for flowcontrol setup, and reliability for host-to-host communications. Thesession layer provides the mechanism for opening, closing and managing asession between various end-user applications through various requestsand responses that take place among such applications. This layer isused to maintain and—if necessary—recover a connection through eitherfull duplex or half-duplex operation, as well as through the use ofsynchronization in the stream of exchanged messages. The presentationlayer acts as the data translator for the network through the formattingof information to the application layer for downstream activities suchas information displaying or additional processing. In so doing, itavoids having to have the application layer (discussed below) resolvesyntactical differences in data representations within various end-usersystems. The application layer acts as the user interface responsiblefor displaying received information to the user by standardizingcommunication based on activities within the underlying transport layerprotocols. In this way, it can establish and manage respective datatransfer channels and data exchange between network components. In oneform, such interface may include those for visualization, such asthrough a display. More particularly, while wearable electronic device100 and gateway 300 may be arranged at least in part on the OSI layercommunication model, it will be appreciated that different combinationsof layers could be used within a given protocol stack.

In another form, the conceptual model on how at least a portion of thesystem 1 and the wearable electronic device 100 are arranged tostandardize their various inter-node communication functions follows theTCP/IP protocol. The TCP/IP model has four abstraction layers that areused to generally describe design guidelines and implementations ofspecific protocols for network-based communications; these are theapplication, transport, internet and network access layers. Theapplication layer provides applications with the ability to access theservices of the other layers and defines the protocols that applicationsuse to exchange data; examples of the application layer include filetransfer protocol (FTP), the previously-mentioned HTTP and simple mailtransfer protocol (SMTP), among others. The transport layer (an exampleof which is transmission control protocol (TCP)) accepts data fromapplication layer, and then divides it up for subsequent conveyance tothe network layer through host-to-host logical connection. The internetlayer provides logical addressing, path determination for the segmentsto be sent and forwarding to ensure that the segment is moved across thenetworks to a destination network. One common protocol of this layer isthe internet protocol (IP). The network access layer provides theprotocols and hardware required for connection of a host to a physicalnetwork (such as a LAN or WAN), as well as to deliver data across suchnetwork. As with the previously-discussed OSI model, the use of theTCP/IP model to describe the communications functionality of at leastportions of one or both of the wearable electronic device 100 and system1 is beneficial in that it allows device independence through describingsuch functionality without regard to the underlying physical structureof the various components. It will be appreciated that both of the OSIand TCP/IP models are within the scope of the present disclosure.

Generally, the three top layers in the OSI model—the application,presentation and session layers—correspond to the single applicationlayer in the TCP/IP model such that they are grouped together in thelatter, while the two lowest layers—the data link and physicallayers—correspond to the single network access layer in the TCP/IPmodel. Regardless of which model is employed, it will be appreciatedthat the uppermost layers (i.e., those that correspond to theapplication, presentation and session layers of the OSI model, as wellas those that correspond to the application layer of the TCP/IP model)are those that are logically closer to the end user, while the lowerlayers (such as the data link and physical layers of the OSI model andthe network access layer of the TCP/IP model) are logically closer tothe physical transmission of the data. Thus, when the wearableelectronic device 100 utilizes LoRa-based communication between it andthe gateway 300, the LoRaWAN defines the communication protocol andsystem 1 architecture for the network while the LoRa physical layerdefines the long-range communication link.

In another conceptual way of visualizing the structure of the system 1when used in conjunction with the wearable electronic device 100 is asan IoT device (as disclosed later in conjunction with FIGS. 3A and 3B)as part of a cloud 500 infrastructure. In such understanding, thestructure may be defined with five major layers, including a devicelayer for the various components and firmware, a communication layer forradio-based connectivity with one or more networks through the gateway300, server 400 or the like, a cloud services layer (also referred to asa software layer) to receive and analyze the data at scale, anapplication layer and a security layer.

With particular regard to the system 1 of the present disclosure, someof the sensing and hybrid wireless communication module 175 activitiesmay not require the services and functionality of some of these higherlayers (also referred to as middleware layers made up of the transport,session and presentation layers), and as such may build applicationsdirectly on top of the network layer of the seven layer stack of the OSImodel, or the internet layer of the four-layer stack of the TCP/IPmodel.

By way of example, under either an OSI or TCP/IP model or protocolstack, one or more data processing layers may define signal processing,data analytics or the like where the acquired raw LEAP data from anindividual is cleansed and placed in a more structured form—often withreduced dimensionality—such that it can be manipulated by the logicdevice 173 (or its equivalent in system 1) for subsequent featureextraction, testing and use in predictive analytics such as thoseassociated with one or more of the machine learning models discussedherein. In one form, the various data processing layers associated withmodels that provide analytics to system 1 may be included with—or formedto cooperate with—a lower (such as physical) layer that may includevarious hardware components such as sensors 121 that collect and conveysignals that correlate to event data. In this way, the organization ofevents into data structures storable in memory 173B of the wearableelectronic device 100 (or its equivalent memory 173B in system 1) mayinclude various forms of data tables with labeling or identification,such as the type of event being sensed or detected, a particularactivity category (such as ADL) of the person being monitored,spatio-temporal contextual information for an event, a scalar value ofthe sensed event, as well as others.

In addition to events, other tangible things may likewise be organizedas a data structure. For example, various systems of the human body(such as the urinary tract) may be modeled as one or more datastructures with input features (suitably configured, for example, invector form) that may include various physiological data such as thatdiscussed elsewhere in this disclosure. As is discussed elsewhere in thepresent disclosure, other forms of the LEAP data may be used inconjunction with the physiological data in order to help infer whetheran individual that is associated with the wearable electronic device 100is at risk of developing an adverse health condition based on aquantifiable mathematical interaction of the defining attributes of suchthings with one or both of the event data that is collected by thewearable electronic device 100 and baseline data that may be eithertaken from the wearable electronic device 100 or a lookup table or otherlocal or remote source of such data.

In one form, the system 1 is part of a network equipped with one or moreapplications including a caregiver application, configurationapplication and family application, all of which may be operated overthe internet, a conventional cellular (i.e., 3G, 4G, GSM) network or thelike through the remote computing device 900 that will be discussed inmore detail in conjunction with FIGS. 4, 9 and 10A through 10F. In oneform, the configuration application may be used for initialization,setup, debugging or the like, the caregiver application may be used forresponding to alerts or messages pertaining to the individual beingmonitored (and as such may include caregiver location or proximityfunctionality in a manner as described elsewhere in this disclosure),while the family application may be used in a manner roughly similar tothe caregiver application in that it may receive a message or alert butwithout a caregiver location or response feedback.

Portions of the system 1, as well as portions of the network, mayinclude devices, components or the like that are configured to operatein various network layers. By way of non-limiting example, one or bothof the servers 410, 420 may include modules configured to work within anapplication layer, a presentation layer, a data access layer (DAL) and ametadata layer. Similarly, one or both of the servers 410, 420 mayinclude access to one or more data sets that make up a data layer. Thus,various data sets may be stored on one or more data storage devices. Oneor more web-based APIs—such as those associated with I/O interfaces—mayoperate in the application layer. Likewise, one or both of the servers410, 420 may include components and various hardware or software modulesthat work in the presentation layer, where they can support various webservices and functions. In one form, a web application, web service orthe like may access some or all of the data sets through the data accesslayer that may in one form be divided into one or more independent DALsfor accessing individual ones of the data sets. In one form, theseindividual DALs are known as data sockets or adapters. The DALs mayutilize metadata from the metadata layer to provide the web applicationor the web service with specific access to the data sets. For example,the one or more DALs may include operations for performing a query ofthe data sets in order to retrieve specific information for the webapplication or web service. In a more particular form, the DAL or DALsmay include a query for patient records associated with a wearer of thewearable electronic device 100, especially in situations where changesin the salient indicators of the patient's health are identified orsuspected. Within the present disclosure, the phrases “salientindicators of the patient's health”, “indicia of a health condition” andtheir variants describes one or more components of the LEAP data thatwhen analyzed by a processor 173A in response to executable instructionsstored in a non-transitory computer readable medium (such as memory173B) provides information that may be output to a caregiver in such away that enables the caregiver to predict in advance of—rather thanreact after the onset of—deleterious changes in the health of thepatient. Likewise within the present disclosure, it will be understoodthat the discussion of the four components of the LEAP data refers toany or all such components, whether individually, in part or in toto,and that their identification as such will be apparent from the context.Moreover, the analysis of the LEAP data, such as by one or more of themachine learning models discussed herein, can produce biomarkers thatcorrelate to one or more quantitative estimates of such a healthcondition, and as such include clinically-relevant information. As willbe discussed in more detail later, these biomarkers may be based oncomparisons with baseline data, statistical norms, commonly-acceptedclinical scores or the like.

Referring next to FIGS. 2A through 2I, details associated with twodifferent embodiments of the wearable electronic device 100 are shownwhere FIGS. 2A through 2H depict a first of these embodiments and FIG.2I depicts a second of these embodiments. In one form of the firstembodiment, the wearable electronic device 100 is configured to be wornon the wrist of the patient such that it defines a band or relatedwristwatch-like form factor.

Referring with particularity to FIG. 2A, the wearable electronic device100 includes a main housing assembly made up of the housing 110 and thesupport tray 120. As such, one or both of the housing 110 and supporttray 120 act as a worn platform upon which some or all of the remainingcomponents that make up the wearable electronic device 100 may besupported, secured or otherwise affixed. The housing 110 includes acentral body, as well as two opposing lateral extensions 111, 112. Aswill be discussed in more detail below, these lateral extensions 111,112—in addition to providing a mounting location for a strap (such asthe one shown in FIG. 2H as a conventional NATO-style band 190) such asthat used with a wristwatch—may provide a trough or cavity-like recessinto which the sensors 121, antennas 140 or other electrical orsignal-based components may be placed. Formed in a side edge of the bodyof the housing 110 is a slot 113 that can be used to allow a fingernailor small sharp object to be inserted as a way to unlock the housing 110from a top cover 130.

Referring with particularity to FIGS. 2B through 2E, variouspartially-assembled views are used to show the construction of thehousing 110, support tray 120 and certain electronic and structuralcomponents. For example, the shape of the support tray 120 defines acavity 120C where the electronic components reside upon assembly of thewearable electronic device 100. In one form factor, the underside of thewearable electronic device 100 as defined by the support tray 120 mayinclude different contours or different sizes in order to accommodatedifferent wrist sizes of an individual wearer, as well as slots 122, 123through which the band 190 (as shown in FIG. 2H) may pass. For example,different contours may include those defining relatively small, mediumand large radii of curvature. In another form factor, an adapter may beeither permanently or removably affixed to the housing 110 or supporttray 120 in order to provide a more secure fit to corresponding small,medium or large wrist sizes. When the housing 110 and support tray 120are joined together, such as by a snap-fit connection, gluing, frictionfit or the like, the cavity 120C that is formed provides a volumetricspace for the mounting of the various electrical and structuralcomponents such as some of the sensors 121, logic device 173, hybridwireless communication module 175, battery 180 or the like. This modularconstruction of the housing 110, support tray 120 and the variousstructural components, as well as the electrical components that will bediscussed in more detail in conjunction with FIGS. 2F and 2G, allows thewearable electronic device 100 to have varying degrees of sensingfunctionality, depending on the end-use needs. For example, if a largernumber (or a large number of different types) of physiological sensors121C (shown in FIG. 2F) are needed for particular forms of bodilyfunction monitoring, different modular packages or options made ofdiffering combinations of such sensors may be placed within the housing110 and support tray 120. In one form, this modularity may be enhancedthrough structure that can accept various smaller components orcomponent sets. For example, and as shown in FIG. 2E, a ledge 114 mayact as a mounting surface to a complementary-sized and shaped undersideof the top plate 130, thereby promoting a volumetric space for thesecure placement of one or more smaller components.

Referring with particularity to FIGS. 2F and 2G in conjunction with FIG.1, the electronic components that make up the power, processing,communication and sensing functions of the wearable electronic device100 are shown in a disassembled (exploded) view in FIG. 2F and anas-assembled view in FIG. 2G. The components include a flex circuit 150with ERM charging coil, hall effect proximity and capacitance sensor, astructural mid-frame 160 and printed circuit board (PCB) assembly 170that acts as a mount for the battery 180 and one or more of the logicdevice 173 (that may include one or more of a processor 173A, memory173B, bus 173C, input/output 173D) and the hybrid wireless communicationmodule 175 that attains its hybrid status by virtue of being made up ofvarious wireless communication sub-modules 175A, 175B and 175C. One ormore of the hybrid communication sub-modules 175A, 175B and 175C, aswell as portions of the logic device 173 may be formed (such as byprinting) on a single substrate of the PCB assembly 170. In one form,the logic device 173 may include various ancillary components such asfilters, amplifiers, limiters, modulators, demodulators, datatransmission signal conditioners, analog-to-digital anddigital-to-analog converters or the like in order to perform the variousprocessing, control, communication, and related operations as describedherein.

The GNSS 10 of FIG. 1 includes a set of satellites (only one of which isshown) that determine the latitude and longitude position of thewearable electronic device 100 through well-known methods. A secondwireless communication sub-module 175B contained within the wearableelectronic device 100 is configured to act as a receiver of signals fromGNSS 10; such signals are used to determine location when the patient orother person wearing the wearable electronic device 100 is not in rangeof any of the BLE beacons 200. Machine code 173E (such as that residenton memory 173B) may cause the third wireless communication sub-module175C to preferentially transmit the data received by the first wirelesscommunication sub-module 175A when the wearable electronic device 100 iswithin a predetermined distance from a source of a signal emanating fromthe corresponding BLE beacon 200, as well as cause the third wirelesscommunication sub-module 175C to preferentially transmit the datareceived by the second wireless communication sub-module 175B when thewearable electronic device 100 is beyond the predetermined distance froma source of the BLE beacon 200 signal.

In one form, processing and related computation can take place on anarchitecture that is based on the processor 173A that is in the form ofone or more central processing units (CPUs). In another form, processingand related computation can take place on an architecture that is basedon an application-specific integrated circuit (ASIC). In another form,processing and related computation can take place on an architecturethat is based on a field-programmable gate array (FPGA). In stillanother form, processing and related computation can take place on anarchitecture that is based on a graphical processor unit (GPU) such asthe Tesla V100 or GTX 1080 series, both manufactured by NVIDIACorporation of Santa Clara, Calif.; processors such as GPUs and ASICsare particularly beneficial when the calculations being performedinvolve large-scale matrix multiplication, such as those associated withdeep learning neural networks or other machine learning approaches wherelarge amounts of parallel data may need to be processed. In stillanother form, a tensor processing unit (TPU) such as those beingdeveloped by Google to be used in conjunction with its open-sourcelibrary TensorFlow may be used specifically for neural networkapplications, particularly for its ability to conduct analytics not justin the cloud 500, but also as part of autonomous or semi-autonomoushardware that is specific to system 1 or wearable electronic device 100.As will be discussed in more detail later, GPU-based processing may beused as a training tool as part of a deep learning neural network as away to extract meaningful health-related analytics from the large amountof acquired data from the wearable electronic device 100. All of theseand their variants are deemed to be within the scope of the presentdisclosure. Although not shown as part of system 1, it will beappreciated that the logic device 173 and its various components (suchas processor 173A, memory 173B, bus 173C, input/output 173D and relatedmodules or components) and the concomitant functionality may besimilarly replicated in other parts of the system 1, such as the servers400, as well as in the cloud 500. Such replication may include scalingup the number of such logic devices 173 on an as-needed basis forsituations, such as those associated with running one or more analysesof the health condition of the person being monitored as will bediscussed in more detail later. In one form, an analysis may include aclinical decision support (CDS) model that helps a caregiver use thedata being acquired by the wearable electronic device 100 in order todetermine, among other things, adverse changes in the health of a personbeing monitored. Depending on the complexity of the inquiry, CDS—whetheror not as part of a machine learning model—may involve the use ofmassive parallelism or related increases in computational power, makingthe use of the processing and related computational power discussedherein particularly beneficial. It will likewise be appreciated thatconceptually these various components of the logic device 173 may begrouped or otherwise arranged in various ways to define circuit-basedmodules to perform specific tasks or subtasks associated withidentification of changes in the one or more salient indicators ofchanges in the health of an individual that is associated with thewearable electronic device 100. In one form, such modules may bearranged to perform specific tasks (for example, instruct the sensors121 and other equipment to acquire data, perform calculations to filter,extract and process the data, as well as control the communication ofthe data, in addition to performing fail-safe checks, system or devicemonitoring, or the like) in a manner analogous to the hybrid wirelesscommunication module 175. For example, the logic device 173 may includeone or more of a sensing module, a prediction module (including havingor otherwise being cooperative with machine learning-based algorithmsand ensuing models) or an alert module, among others.

By configuring one or both of the wearable electronic device 100 and thesystem 1 with components such as these, a measure of customization ofthe particular architecture—as well as improvements in system 1functionality—may be achieved in a manner not possible with a general,all-purpose computer. In one form, GPUs or FPGAs may be used inconjunction with a model (such as a machine learning model as discussedin more detail later) to compute predicted outcomes derived from thedata being acquired by the wearable electronic device 100. In one form,a GPU-based approach may be used in conjunction with deep learninglibrary-based frameworks (such as TensorFlow, Microsoft CognitiveToolkit, PyTorch or the like) to train, validate and test certainmachine learning models (such as deep learning models) that arecomputationally-intensive. In such case, these libraries may useadditional libraries (for example, the deep learning library Keras) toorganize layers of a deep learning neural network model as a way toexpedite the analysis of the LEAP data. As such, high-level neuralnetwork APIs like Keras or related libraries help to simplify the amountof code that is required to train a neural network, and may be used incooperation with Theano, TensorFlow or other back-end frameworks. Inanother form, FPGAs are useful in training scenarios where the acquireddata may or may not be ordered in the structured, array-based form thatGPUs are accustomed to receiving. Regardless, GPUs and FPGAs are bothconstructed with numerous simpler cores compared to CPUs, and as suchmay be particularly well-suited for performing large-scale distributedprocessing tasks each involving relatively simple computations in amanner that CPUs (with their central-processing and managementfunctionality) might not; such large degree of distributed processing isparticularly beneficial in situations such as those associated withmachine learning workloads. On the other hand, CPUs may be beneficial insituations where the libraries (such as Scikit-Learn) might not provideany GPU support and where the inclusion of such support might compromisethe ability to function with differing hardware and operating systemcombinations.

The logic device 173 is configured to receive data from one or moresensors 121 and provide logic-based instructions to one or both of thewearable electronic device 100 and the system 1. As will be appreciatedby those skilled in the art, the processor 173A forms the primarycalculation functions of the logic device 173, and as such may be asingular unit such as the CPU, ASIC, GPU or FPGA discussed previously,or one of a distributed set of units. In addition to being resident onthe wearable electronic device 100, one or more equivalent processors173A may be present in other parts of system 1, including the server400, as well as in signally-coupled computers or related components inthe cloud 500. In one form, such processor or processors 173A may beprogrammed to perform machine learning functions, such as through atrained artificial neural network to determine, among other things,whether a patient associated with a particular wearable electronicdevice 100 is at risk of developing an infection or other adverse healthcondition, as will be described in more detail elsewhere within thisdisclosure. As such, the processor 173A may comprise or be coupled tomemory 173B for storing not only programmed instruction sets, but alsomachine code that makes up such a neural network, K-means clustering orother machine learning model. In one form, bus 173C provides theelectrical or signal connectivity for the various components within thelogic device 173, and in one form may also provide the connections toand from the logic device 173 for the sensors 121, hybrid wirelesscommunication module 175, battery 180 or other components within thewearable electronic device 100.

A structural description of the elements that make up the variouscomputer software features is described next. Within the presentdisclosure, the organized collection of instructions and computer datathat make up such software includes both API software and systemsoftware (such as operating system software and basic input/output thatrelates to the operation of the computer itself). In one form, theapplication software acts as an interface between a user and the systemsoftware, while the system software acts as an interface between theapplication software and the computer hardware. In one form, both thesystem software and the application software may be stored on memory173B. Taken in their totality, such software provides programmedinstructions that may be implemented on the logic device 173 to allow itto interact with the wearable electronic device 100, system 1 or othercomputer-based equipment in order to perform one or more of the LEAPdata acquisition, processing, communicating, analysis and relatedfunctions disclosed herein. For example, source code created by aprogrammer may be converted into executable form as machine code 173Efor use by the processor 173A; such machine code 173E is predefined toperform a specific task in that they are taken from a machine languageinstruction set known as the native instruction set that may be part ofa shared library or related non-volatile memory that is specific to theimplementation of the processor 173A and its particular Instruction SetArchitecture (ISA). As with the previous discussion of the OSI andTCP/IP conceptual models on system 1 and wearable electronic device 100to describe communication functional operations between adjacent layers,the ISA provides a conceptual model of the layer that describes thespecific implementation of logic device 173 in such a way to correlatevarious programming functional operations. Thus, whereas the OSI modelassumes seven conceptual layers, in one form the ISA is one of four, asabove it are the application software layer and the system softwarelayer, and below it the hardware layer. Among other things, the ISA isresponsible for organization of memory and registers, data structuresand types, what operations are to be specified, modes of addressinginstructions and data items, as well as instruction encoding andformatting. Thus, the ISA acts as an interface between the hardware ofthe processor 173A and the system or application software through theimplementation of the machine code 173E all of which are predefinedwithin the ISA. As such, the machine code 173E imparts structure to thesuccessive architectures of processor 173A, logic device 173, PCBassembly 170 and wearable electronic device 100, specifically in theform of a program structure that may be made up of a set of individualcodes that together may be depicted herein as a flow diagram or relatedsequence that operates on the data structure that itself may be in oneform an organized list, array, tree or graph of collected LEAP data. Inone form, such a structural relationship exists between the processor173A, the memory 173B and the machine code 173E regardless of whetheradditional computational activities (such as those associated with themachine learning algorithms and models that are discussed elsewhere inthis disclosure) are or are not being used. As such, softwareinstructions as embodied in the corresponding portion of the machinecode configure the logic device 173 to provide the functionality asdiscussed herein.

With this understanding, the machine code 173E taken from the nativeinstruction set in turn is arranged as a specific structural conduit toallow the processor 173A and memory 173B to communicate with particularsystem or application software. Accordingly, the predefined structureembodied in the machine code 173E allows them to become a part of theprocessor 173A in a necessary way such that together they can implementunique operations in response to the particular commands from theprogram structure; such operations may include data handling operations,arithmetic operations, control flow operations, addressing operations,logic operations and memory location operations, as well as others. Inone form, the machine code 173E may be made to reside on memory 173B tofacilitate cooperation with the processor 173A. In this way, the machinecode 173E forms a machine-specific symbiosis with the processor 173A andmemory 173B to delimit the operation of the logic device 173 through thecreation of numeric values that can only have meaning imparted to themthrough this necessary relationship. More particularly, this allows thelogic device 173 to—among other things—cause the sensors 121 and hybridwireless communication module 175 of the wearable electronic device 100to detect one or more forms of wearer-specific LEAP data and wirelesslytransmit such data to a remote receiver using an LPWAN signal.Furthermore, this allows the logic device 173 to implement—or cooperatewith—a machine learning classification model (such as a neural network,decision tree, Bayesian network or other approach as discussed elsewherein this disclosure) to automatically perform predictions of potentiallyadverse health conditions for a person from whom one or more forms ofthe LEAP data is being acquired via the wearable electronic device 100.Likewise, the data that is being input to, manipulated by or output fromthe wearable electronic device 100 (including the data acquired by thevarious sensors 121), as well as any data that is operated upon by sucha machine learning classification model, may be stored in memory 173B asdata structures or related contents in the form of arrays (includingvectors as their one-dimensional tensor variant, matrices as theirtwo-dimensional tensor variant and tabbed matrices as three-dimensionaland subsequent n-dimensional variants such as so-called notebook tabs),link lists, stacks, queues, tree structures, graphs, or the like. Inthis way, these multidimensional arrays differ from tensors in thatwhile they are both types of objects, the tensor is a type of functionwith an array of numbers arranged on a regular grid with a variablenumber of axes while the multidimensional array forms a data structurethat may be used to represent the tensor in a coordinate system, therebygiving it unique higher dimensional attributes.

Examples of data such as the LEAP data acquired by sensors 121 andstored within a portion of memory 173B may include raw data, processeddata, time-stamped data, baseline data or the like. As such, theprocessor 173A and memory 173B cooperate to execute or otherwise use thedata structures that correspond to the object being described or theevent being performed. In particular, the CPU, ASIC, GPU, FPGA orrelated processors may execute program instructions by utilizing stateinformation of such program instructions as a way to make the electroniccircuitry associated with the logic device 173 into a special-purposemachine. Within the present context, such data structures form aconcrete embodiment of a space that is formed within memory 173B by oneor more particular abstract data types (ADT) that are used to specifythe operations (that in one example are configured as thepreviously-mentioned program structures) that can be performed on thedata structure. Diagrammatic representations of some of these datastructures will be discussed elsewhere in this disclosure in conjunctionwith FIGS. 3A through 3K, as well as FIGS. 8, 12 and 13.

In one form, the CPU, ASIC, GPU or FPGA may include varying degrees ofcomponent integration, including that of the processor 173A and memory173B in order to mimic system-on-a-chip configuration where scenariossuch as those encountered in Bayesian-like decision making (that is,finding the likelihood of an event given historical or experientialinformation about similar events) for determining the health of a personbased on a statistical analysis of the data gathered by the wearableelectronic device 100 may be encountered. As such, various machinelearning models (including the ones discussed in more detail later) maybe executed by such a system-on-a-chip configuration, including forsituations where cloud-based training (such as that implemented onsystem 1) is implemented. For example, the memory 173B may includerandom access memory (RAM), read-only memory (ROM) and flash variants aspart of a computer readable medium that can store data structures,program modules, machine codes, native instruction sets, computerreadable instructions or other data. Furthermore, the memory 173B maystore a trainable machine learning algorithm that can be accessed andexecuted by the processor 173A in order perform a classification,regression or clustering-based model or analysis, as well as to updatethe state of the machine learning algorithm through correspondingupdates to the memory 173B. In a particular form, the memory 173B can beconfigured to store in-memory analytics such that the machine learningalgorithms and related models can be made to run in or be shared bymemory 173B such that direct memory access of the analytics is enabled,thereby providing the ability to perform more real-time calculations onthe acquired LEAP data. Likewise, the data being evaluated by thein-memory analytics can be spread across numerous parallel ordistributed computing processors 173A as a way to divide larger amountsof data processing to further promote real-time activities.

The various sensors 121 may be advantageously located on or inside thewearable electronic device 100 such as in or on the housing 110, lateralextensions 111, 112, support tray 120 or the like. As such, the sensors121 are non-invasive in that they need not be ingested or inpercutaneous, subcutaneous or intravenous form. In one exemplary form,some sensors 121 that are shown generally as being embedded in supporttray 120 may otherwise be placed anywhere in or on the wearableelectronic device 100 in such a manner as to facilitate acquiring datathat in turn may be used by a behaviorist model (including machinelearning and CDS variants) that can run as a set of instructions on thesystem 1 in order to correlate, manipulate and transform the data into aform such that it can provide indicia of one or more LEAP traitsassociated the wearer of the wearable electronic device 100. In oneform, the sensors 121 may act in conjunction with one another—as well aswith instructions that are stored on a machine-readable medium such asmemory 173B—to aggregate (or fuse) the acquired data in order to infercertain activities, conditions or circumstances. Such sensor fusion cansignificantly improve the operability of the wearable electronic device100 by leveraging the strengths of each of the sensors 121 to providemore accurate values of the acquired data rather than if only comingfrom one such sensor 121 in isolation. For example, rotation-basedgyroscopic measurement alone can lead to accumulating errors, while theabsolute reference of orientation associated with accelerometers andmagnetometers may be prone to high noise levels. By fusing the acquiredraw data, the sensors 121 and accompanying data-processing instructionscan filter the information in order to compute a single estimate of (sixdegree-of-freedom, 6 DOF) movement, orientation or position, which inturn simplifies downstream computational requirements. For example, suchfusing may occur through integrating the orthogonal angular-rate datafrom the gyroscopes in order to provide orientation information, andthen measuring the linear acceleration vectors within a particularwearer frame of reference and then rotating into wearer navigationcoordinates using a rotation matrix as determined by the gyroscopes inorder to remove gravitational effects.

In one form, feature extraction of acceleration-related data may takethe form of normalizing individual Cartesian axis input vectors forgravity compensation. After being gravity-compensated, the accelerationdata may be double-integrated in a manner similar to the gyroscopic datain order to provide suitable location or position information, whilecombining such measurements with other location-detecting means (such asGNSS or BLE, both as discussed herein) may be used to help avoid driftthat may result from accelerometer bias error or gyroscope rotationerror. Likewise, applying temporal filters may help to extract suitablemovement feature information. By way of example, the movement data(which in one form may include falls, high or low gait speed or otherforms of movement) acquired from the accelerometer and gyroscope may beused in order to determine whether sudden changes in motion are present;such an approach may include the calculation of derivatives, movingaverages or the like in order to make it useful for one or moreclassification or regression machine learning models such as a decisiontree-based or random forest-based classifier. Likewise, a portion of themachine code may be dedicated to controlling sampling intervals forvarious forms of the LEAP data, particularly those data signals thathave significant temporal components such as activity data. In onenon-limiting example, such sampling intervals can be made to take placein various fractions of a second (such as 0.01 second, 0.1 second or thelike), various multiples of a second (such as 1 second, 2 seconds, 5seconds, 10 seconds or the like) or as fractions or multiples of largertime intervals, depending on the accuracy needs associated with the databeing acquired. In one form, both the presently-acquired data and anyhistorical or baseline data (including those with significant temporalcomponents as discussed herein) may be placed in memory 173B in order toprovide appropriate signatures that correspond to the LEAP data forsubsequent comparison or analysis purposes.

In one form, accelerometers, gyroscopes and related MEMs-basedembodiments of sensors 121 are used to acquire baseline data for eitheran individual patient or from a group of patients the latter of whichmay be representative of a particular patient's demographics (such as byage, weight, gender, activity level, known health conditions or thelike). For example, activity-based baseline data may include knowninter-patient or intra-patient activity that is taken from previousactivity signatures that are stored in memory 173B; such data may beeither acquired through prior operation of the sensors 121 or taken froma historical record such as contained within a lookup table or the like.In one form, baseline activity data such as that acquired from sensors121 that are in the form of accelerometers, gyroscopes and the like maybe created through examples that can be correlated to known movements ofthe individual being monitored. For example, the individual may gothrough various sitting, standing, walking, running (if possible) andrelated movements that can be labeled for each activity whereclassification is desired. As will be discussed in more detail later,such labeling may be useful in performing supervised machine learning,particularly as it applies to training a machine learning model.

Understanding that location data is acquired significantly orexclusively through the hybrid wireless communication module 175, in oneform, the sensors 121 may be placed into three major groups for theacquisition of the other components of the LEAP data. In one form,sensors representative of these other three major groups includeenvironmental sensors 121A, activity sensors 121B and physiologicalsensors 121C. For example, activity sensors 121B that are used tocollect activity data may include accelerometers, gyroscopes,magnetometers or the like, while the environmental sensors 121A used tocollect environmental data may include those configured to acquiretemperature, ambient pressure, humidity, carbon monoxide, carbondioxide, smoke or the like, and the physiological sensors 121C used tocollect physiological data may include those configured to acquire heartrate, breathing rate, glucose, blood pressure, cardiac activity,temperature, oxygen saturation, smells (such as total volatile organiccompounds (TVOC)) or the like. Additional ones of sensors 121D used forother functions, such as cameras, microphones, wear-detection sensors orthe like may also be included, and all such sensors and their relatedmodalities are deemed to be within the scope of the present disclosure.As such, numerous combinations of sensors 121A, 121B, 121C and 121D maycontribute to a fusion of the acquired data in order to improve theaccuracy of the inferred event.

Moreover, the acquired data—which is received in raw form—maysubsequently be labeled in order to correlate it to one of the LEAP datacategories. Such labeled data may then correspond to an event that maybe further grouped according to time, date, type of sensor or the likethat may be stored in memory 173B. In one form, the events being storedin memory 173B may be in feature vector form, and may includeactivations and learned weights for each one of the sensors 121. Thememory 173B may further store a training data set and a validation dataset that in one form may be taken from the same acquired LEAP data set,as will be discussed in more detail later. In one form, the wirelesscommunication sub-modules 175A, 175B and 175C may be formedindependently of the sensors 121, while in another, each wirelesscommunication sub-module 175A, 175B and 175C may include its ownintegrated, dedicated sensor or sensors (not shown). Furthermore, theplacement of the sensors 121 relative to the various wirelesscommunication sub-modules 175A, 175B and 175C as shown in FIG. 2F is forvisual convenience, and that it will be appreciated that suchplacement—as well as that of other components of the wearable electronicdevice 100—may be dictated by utilitarian concerns, and that allvariants of such placement is deemed to be within the scope of thepresent disclosure.

Relatedly, the LPWAN-based RF signal transmission features (as well asthe GNSS and BLE receiving features) may be embodied in various forms,including as a device, module or chip, and that all such variants aredeemed to be within the scope of the present disclosure. Regardless ofthe manner in which the various sensors 121 and wireless communicationsub-modules 175A, 175B and 175C are packaged within the housing 110 orsupport tray 120 of the wearable electronic device 100, the transmissionof data signals, control signals or the like from the third sub-module175C to the gateway 300, backhaul server 400 or related receiver in thesystem 1 may be made in a direct wireless manner without the need forintervening near-field wireless communication components and attendantcomplexity. For example, the use of LPWAN allows the wearable electronicdevice 100 to avoid the extra architectural complexity associated withmaster/servant relationships such as those where the collected data mustfirst be transmitted a short range through wearable signal relay units,repeaters or the like, even if such units or devices are situated nearthe data collecting sensor (such as those situated on another part ofthe body of the individual being monitored). Thus, by forming thevarious sensors as part of the wearable electronic device 100 (such asby direct mounting to the device's chassis, substrate, housing 110 orthe like, or indirectly through a PCB or other component that is itselfdirectly mounted to the chassis, substrate or housing 110), size,weight, packaging and related architectural efficiencies can berealized. Moreover, portions of the wearable electronic device 100 thatcorrespond to the first wireless communication sub-module 175A—whileused primarily for receiving BLE beacon 200 signals to determinelocation—may also be used to receive configuration parameters andfirmware updates from a suitably-equipped mobile device such as thatwhich will be discussed in conjunction with FIGS. 4 and 5.

In one form, some of the electronic components of the wearableelectronic device 100 include:

-   -   additional memory (such as that associated with EEPROM        Serial-I2C 10K-bit 10K×1 2.5V/3.3V/5V 8-Pin UDFNT/R, as        manufactured by Microchip/Amtel as part number        ATECC508A-MAHDA-T);    -   the wireless charger 600 (such as that associated with Qi (WPC)        Compliant Highly Integrated Secondary-Side Direct Lithium Ion        Charger, 4 V Min Input, 28-pin DSBGA (YFP), Green (RoHS & no        Sb/Br, as manufactured by Texas Instruments as part number        BQ51050BYFPR);    -   a haptic motor (such as that associated with VIBRATION MOTOR        COIN 3V FLEX, as manufactured by Jinlong Machinery & Electronics        as part number C0720B001F);    -   a capacitive sensing controller chip (such as that associated        with CAPSENSE EXPRESS CONTROLLERS WITH SMARTSENSE AUTO-TUNING 16        BUTTONS, 2 SLIDERS, PROXIMITY SENSORS, as manufactured by        Cypress as part number CY8CMBR3108-LQXIT);    -   one or more accelerometers (such as that associated with        microelectromechanical (MEMS) Digital Output Motion Sensors:        Ultra-Low-Power High-Performance 3-Axis “Femto” Accelerometer,        1.71 to 3.6 V, −40 to 85 degrees C., 12-Pin LGA, RoHS, Tape and        Reel, as manufactured by STMicroelectronics as part number        LIS2DH12TR);    -   a battery level detection chip (such as that associated with        ether IC FUEL GAUGE LI-ION 1CELL 8TDFN or IC COMPARATOR SGL LP        4UCSP, both as manufactured by Maxim as part numbers MAX17048G+        and MAX9063EBS+TG45 respectively);    -   a BLE radio and microcontroller module to correspond to the        first wireless communication sub-module 175A (such as that        associated with MOD BLE 4.2 NORDIC NRF52832 SOC, as manufactured        by u-blox as part number nINA-B111);    -   a GNSS receiver module to correspond to the second wireless        communication sub-module 175B (such as that associated with        Module: GPS GLONASS; ±1.5 m; I2C, SPI, UART; −165 dBm; −40° C.        to −85° C.; SMD, as manufactured by Origin GPS as part number        ORG1510-MK04);    -   a LoRaWAN™ tx/rx module to correspond to the third wireless        communication sub-module 175C (such as that associated with RF        TXRX MODULE ISM<1 GHZ SMA ANT, as manufactured by Microchip as        part number RN2903-I/RM095);    -   a signal expander (such as that associated with IC PORT EXPANDER        18 BIT 25 FIPCHIP, as manufactured by STMicroelectronics as part        number STMPE1801BJR); and    -   a fast charging battery 180 (such as that associated with a        lithium polymer version as manufactured by Alium as a        custom-designed part).

It will be appreciated that these components are merely exemplary, andthat a greater number or a lesser number of components—as well asdifferent components—may be used, depending on the sensory, processing,data storage, wireless communication or other needs of the wearableelectronic device 100. For example, solar cells 141 may be present incertain embodiments of the wearable electronic device 100 to provideelectric power or other source of electric current in a manner generallysimilar to battery 180. In such embodiments, the solar cells 141 may beused as a renewable source of power that would allow the wearableelectronic device 100 to operate for longer continuous time periods. Inone form, such solar cells 141 could be formed as part of the top plate130 or over the strap portion that covers the antennas 140. In anotherform, electric power sufficient to power the wearable electronic device100 may be harvested from the motion of the person wearing the wearableelectronic device 100 during person movement, for example, by swinginghis or her arm while walking, or from the thermal energy generated theperson wearing the wearable electronic device 100, such as throughconductive heat exchange from the skin or other part of the body of theperson to the wearable electronic device 100, including when theconfiguration is similar to the one depicted in FIG. 2I that will bediscussed in more detail later.

In one form, the hybrid wireless communication module 175 is a wirelesscommunications protocol stack that establishes standardized rules for(among other things) wearable electronic device 100 authentication, datarepresentation, signaling, co-existence and error detection. Within thepresent context, the hybrid wireless communication module 175 thatincludes numerous wireless communication sub-modules 175A, 175B and 175Cmay be embodied in different forms as a way to receive and send RFsignals. In one form, each of the various wireless communicationsub-modules 175A, 175B and 175C may be formed as disparate, relativelydiscrete systems that are distinguished from the others by theparticular components or construction, such as those mentioned in theprevious paragraph that may be dictated by the form of communication(such as by frequency, range, mode of propagation or the like) that theyemploy. In another form, each of the various wireless communicationsub-modules 175A, 175B and 175C may share some or all common circuitry,antennas 140, and other features as part of a highly-integrated modulefor configurations or packaging where it is desirable to avoid componentredundancy. It will be appreciated that the choice of how the wirelesscommunication sub-modules 175A, 175B and 175C are configured within thelarger hybrid wireless communication module 175 may be dictated by—inaddition to the aforementioned packing requirements—the particularsignals or data being processed, received or transmitted, and that allsuch variations are deemed to be within the scope of the presentdisclosure. The hybrid wireless communication module 175 includes—orworks in conjunction with—one or more antennas 140 to perform thewireless transmitting and receiving functions between the RF signals andthe various wireless communication sub-modules 175A, 175B and 175C. Inaddition to a flex-based architecture, the antenna 140 may in one formbe configured as a microstrip, patch, shorted patch, planar inverted-F(PIFA), printed, diversity/dipole or any other form-factor suitable foruse with the wearable electronic device 100. For example, in anembodiment where the wearable electronic device 100 is configured as awrist-wearable device (such as depicted in FIGS. 2A through 2H), theantenna 140 may be encased within the laterally-extending ends 111, 112of the housing 110 as a flex antenna 140.

In one form, the various communication wireless communicationsub-modules 175A, 175B and 175C may include corresponding chips orchipsets such as a BLE chipset, a GNSS chipset, WiFi chipset, LPWANchipset or the like. In one form, chipsets used to effect a LoRa-basedconfiguration of the third communication sub-module are manufactured bySemtech Corporation of Camarillo, Calif., and may include (among others)the SX1261, SX1262 or SX1268 chipsets. LoRaWAN™ wireless connectivitybetween the wearable electronic device 100 and the gateway 300, networkserver 410 and application server 420 and related equipment that make upthe remainder of system 1 may be achieved through a suitable lightweightpublish/subscribe messaging transport IoT connectivity protocol, such asa Constrained Application Protocol (CoAP) or a MQTT (Message QueueTelemetry Transport) protocol. CoAP mimics the HTTP that is used fortransmitting web pages, while an MQTT broker-based approach has beenwidely adopted for mobile IoT applications where limited data packetbandwidth or small code footprint remote location connection isrequired. In addition, security can be enhanced through the use ofsecure socket layers (SSL), transport layer security (TLS) or the like.By using an orthogonal sequence spread spectrum mode of transmissionsuch as that associated with the CSS with a spark-gap transmitter asembodied in the chipsets mentioned previously and thepreviously-discussed star topology, significant geospatial coverage maybe achieved with a relatively modest investment in wearable electronicdevice 100 and gateway 300 or related base station infrastructure. Forexample, one or more of the previously-discussed gateways 300 may beplaced through a wide area (such as various places within a city) inorder to take advantage of the range of the wearable electronic device100.

In addition to allowing the wearable electronic device 100 to avoid theextra architectural complexity associated with master/servantrelationships as previously discussed, the use of LPWAN with CSStransmission, multiple sequences and associated star topology promotes amore centralized, simple way to receive and transmit the various dataforms, thereby further improving the way that both the wearable device100 and the associated system 1 may function, especially when the system1 may be operating with a limited link budget. Moreover, the very lowerpower settings of the LoRaWAN™ protocol as discussed herein areparticularly beneficial for the sensors 121 and the wearable device 100that send small packets of data intermittently. In particular, becauseof LPWAN's high link budget, the wearable electronic device 100 can sendthe acquired data at extremely low data-rates over extremely longranges. For example, receiver sensitivity may be as low as −136 dBm,which when combined to an output power of +14 dBm provides for a linkbudget of up to 150 dB. This in turn can lead to the approximately 10miles (or theoretically, up to between 13 and 14 miles) of transmissionrange in direct line-of-sight links and up to roughly 1 to 3 miles inurban environments (where the transmitted signal going may be made topass buildings and related structures). In one form, to promote longerbattery 180 life, each packet (as will be shown and described inconjunction with FIGS. 3A through 3K) being sent through the thirdwireless communication sub-module 175C is relatively small,corresponding to eighty eight bits (i.e., eleven bytes). Likewise, thetotal amount of data being sent over a 24 hour period may be as littleas about 1000 bytes in situations where the individual being monitoredis relatively sedentary, and more if the individual is relativelyambulatory. Moreover, in situations where so-called “true” RTLS isdesired, the message-sending may need to take place with enoughfrequency to ensure adequate coverage. By way of example in such acircumstance, messages may be sent about once per second; in such asituation, this would amount to 86,400 seconds (in a day) times elevenbytes, leading to up to roughly 1,000,000 bytes in a 24 hour period.Furthermore, in order to protect against side channel attacks, allmessages have a LoRa payload length of this eleven byte transmit packetsize. Messages that don't require all eleven bytes may further padunused bytes with the hexadecimal 0x00. In one form, the third wirelesscommunication sub-module 175C may also include various additionalcomponents with which to achieve its LPWAN-based communications; suchadditional components may include a clock or timer, antenna,analog-to-digital converter, digital-to-analog converter, messagegenerator, logical channel selector, packetizer, governor, physicalchannel random selector or the like. Moreover, the third wirelesscommunication sub-module 175C may be configured as a simplex orhalf-duplex device. As such, the wearable electronic device 100 is ableto operate in long-range, low-power situations while remaining connectedto the internet or a related large-scale network.

Referring with particularity to FIG. 2H, in one form, the nurse callbutton 131 is formed on an upper surface of the top plate 130. Withinthe present disclosure, although the nurse call button 131 includes theterm “nurse” in its name, it is understood that such terminology is forsemantic purposes, and that the button is not limited to sending awireless communication to a nurse, but to any other caregiver thatthrough contractual or duty-bound relationship is tasked with providingcare or similar assistance to the individual associated with thewearable electronic device 100. In addition to the nurse call button131, at least a pair of opposing lateral edges 132 of the top plate 130may be made from a transparent or translucent material such that alight-emitting diode (LED) source that may be formed on the PCB 170assembly can be made to pass through the opposing lateral edges 132 inorder to have an outward-illuminating effect. In one form, light pipesmay convey the light from the LED source to the opposing lateral edges132. Although the top plate 130 is shown with an activatable nurse callbutton 131, it will be appreciated that the wearable electronic device100 may be configured with different top plates 130. Such variants mayinclude a frame-like top cover that can contain a family picture, afidget plate or other features. For configurations where the nurse callbutton 131 is an active device, in one form, a small magnet may beincluded as part of the circuitry to have a capacitance sensor.Likewise, in a configuration where the nurse call button 131 feature isnot installed, there is no capacitance sensor or magnet that is sensedsuch that the nurse call feature of the wearable electronic device 100is disabled. In operation, when the nurse call button 131 is activated,a portion of the circuit associated with the nurse call button 131may—through the cooperation of the third wireless communicationsub-module 175C—transmit an alert or related request for assistance overthe low-power wide area network signal such that the request may bereceived by a suitably-equipped remote computing device 900 that will bediscussed in more detail in conjunction with FIGS. 4, 5 and 9.Furthermore an API may be included on the one or more remote computingdevices 900 in order to display additional information, such asinter-room and intra-room data, where such data may correspond to thearchitectural plans or engineering drawings of a particular dwellingsuch as a home, assisted living facility or the like.

If an individual who is associated with the wearable electronic device100 is in distress, he or she presses the nurse call button 131 (shownwith particularity in FIG. 2H) such that an alert may be generated andsent to the remote computing device 900 to indicate one or more of theindividual's identification, location and time of request. In anotherform, a passive variant of the signal being sent from the nurse IDbeacon 200C in response to activation of the nurse call button 131 maybe provided. This passive variant may emit a signal either continuouslyor at intermittent times as a way to show when a nurse or othercaregiver associated with a particular one of the nurse ID beacons 200Cis in close proximity to a patient that is associated with a particularwearable electronic device 100. As will be discussed in more detail inconjunction with providing one or more of a clinical diagnosis and CDSfor situations where the acquired data indicates a worsening of a healthcondition of the individual associated with the wearable electronicdevice 100, the nurse call button 131 may be used to send an alert in aroughly comparable manner over a third wireless communication sub-module175C that makes up the hybrid wireless communication module 175 thatwill be discussed in more detail in conjunction with FIG. 2F. In such anapproach, a generated alert may be sent over the third wirelesscommunication sub-module 175C to at least one caregiver that has thesuitably-equipped remote computing device 900, thereby having the alertfunction in a manner similar to when the person associated with thewearable electronic device 100 has wandered or otherwise moved to alocation that is not permissible or otherwise not advisable. In oneform, the nurse ID beacon 200C may be situated on the remote computingdevice 900.

In one form, when the patient presses the nurse call button 131, thefirst wireless communication sub-module 175A can be awoken or otherwiseactivated in order to detect the presence of an identifier transmissionfrom one or more nearby nurse ID beacons 200C that are installed in orotherwise associated with a corresponding one of the remote computingdevices 900. In addition, an LPWAN-based signal is transmitted throughthe third wireless communication sub-module 175C to the gateway 300 andthen on to one or both of the network server 410 and application server420 for subsequent relay to indicate that the individual needs help, aswell as provide a location of the individual to a caregiver. Thecommunication from the application server 420 to facility staff (nurses,aides, management or other personnel that is uniquely identified with aparticular one of the nurse ID beacons 200C) informs the facility staffthat a patient to whom the wearable electronic device 100 is attached isin need of assistance, while a location of the patient based ondeterminations made by one or the other of the first and second wirelesscommunication sub-modules 175A, 175B is also provided. When a staffmember or other caregiver reaches the patient, they can clear the callby depressing the nurse call button 131, as this instructs the trackerfeature of the first wireless communication sub-module 175A to startsearching for the transmitted identifier from the nurse ID beacon 200Cthat is being worn by the staff member. Because the transmit power ofthe nurse ID beacons 200C is relatively low, the close proximity of theparticular nurse ID beacon 200C of the caregiver that responded to thesignal corresponding to the activated nurse call button 131 will beunderstood by the wearable electronic device 100 to be the only onepermitted to cancel or otherwise close out the request for assistancethat was made by the patient through the nurse call button 131. Inanother form, additional simplicity for the patient may be provided bynot having to have the nurse or related caregiver depress the nurse callbutton 131, but instead clear the alert merely by being in such closeproximity to the patient, or by waving near or touching to the wearableelectronic device 100 by the nurse ID beacon 200C.

In one form, the wearable electronic device 100 may include a lock (notshown) that can prevent the patient from removing the wearableelectronic device 100. The lock, which is disclosed in co-pending U.S.patent application 62/761,961 (the '961 application) and entitledWRISTBAND LOCKING MECHANISM, WRISTBAND, WEARABLE ELECTRONIC DEVICE ANDMETHOD OF SECURING AN ARTICLE TO A PERSON that was originally filedprovisionally on Apr. 12, 2018 the contents of which are herebyincorporated by reference, may be secured using a latch mechanism,magnets, RFID technology, a wireless signal, or any other mechanism thatallows only a caregiver or authorized personnel to remove the wearableelectronic device 100.

In certain embodiments, the wearable electronic device 100 may include ascreen formed in the top plate 130 such that the screen is capable ofdisplaying information collected by wearable electronic device 100,including any alerts generated. Such information may include, forexample, patient's location, information collected by any sensors 121,or information pre-programmed into the memory of the wearable electronicdevice 100 such as the patient's name, address or health information(e.g., illnesses, allergies, medication). In a similar manner, at leastsome forms of data and instruction processing, memory and related datastorage functions that make up a portion of the wearable electronicdevice 100 may be dedicated to—or in some circumstances form a partof—the sensors 121 such that at least some data processing ormanipulation takes place in an edge-like, autonomous manner (that is tosay, without regard to the nature of the system 1 or other informationtransmission and processing backhaul).

Moreover, the acquired LEAP data may be sent from the wearableelectronic device 100 in general (and the third wireless communicationsub-module 175C in particular) to the gateway 300 or other parts of thesystem 1 in substantially unprocessed form, in substantially processedform or in partially processed form. For example, data in substantiallyunprocessed form may include that which is raw in that little or no CPUor other processor-based computation or related manipulation of suchdata takes place prior to being passed from the wearable electronicdevice 100 to the gateway 300 or other parts of the system 1, whereasdata in substantially processed form may include that which experiencesone or more transformative changes (including one or more of cleansingand related preprocessing, extraction and other manipulation asdiscussed elsewhere within the present disclosure) that through the CPU,GRU, ASIC or FPGA and associated algorithmic intelligence places thedata in different or better form for subsequent predictive analytics orrelated use. Thus, in one form, a machine code that is part of the setof machine codes that are stored in the non-transitory computer readablemedium (that is to say, memory 173B or the like) may instruct the thirdwireless communication sub-module 175C to transmit some or all of theacquired wearable electronic device LEAP data without any significanton-device cleansing or related preprocessing, extracting, training,testing or analysis, while in another form, such instructing may beperformed after another portion of the set of machine codes firstconduct some or all of such cleansing, preprocessing, extracting,training, testing or analysis locally (that is to say, on the wearableelectronic device 100) prior to transmitting the data to the gateway300, backhaul server 400 or cloud 500. It will be appreciated that bothvariants are within the scope of the present disclosure.

The wearable electronic device 100 may also be equipped with notifiersconfigured to provide the patient with any alert generated by theprocessor 173A. These notifiers can be in the form of any technologythat would catch the attention of the patient or caregiver to bringattention to the fact that he or she is aware that the processor 173Ahas received information that may correspond to a change in status ofthe patient, as well as other alerts, such as whether the patient couldbe in danger. Some exemplary notifiers include vibration (i.e., haptic)motors, LED lights and an audio speaker. Thus, in one form, the wearableelectronic device 100 is to be able to play spoken voice, music, orsound that may be used to help comfort an elderly wearer of the device,particularly a wearer who may be suffering from dementia. This featurecan also be used to produce an audible alarm when a wearer of the deviceis passing a specific choke point, such as walking out a door in amanner similar to RFID functionality. Thus, when a patient with thewearable electronic device 100 is close to a specific beacon such as oneor more of the previously-discussed elopement beacons 200B, the wearableelectronic device 100 sends an event signal over the internet to thecloud 500 to play an audio file on the speaker. Likewise with displayedimages, such files may be customized with the image or sounds of aperson or persons familiar to the patient, which in turn may have asoothing, calming effect, as well as heighten the cognitive state of thepatient. Relatedly, the wearable electronic device 100 may be configuredto allow for caretakers, family members and other responders tocommunicate directly—and in real-time—with the patient through thedisplay, audio speaker, audio microphone or the like. In one form, sucha message may also be played through one or more of the WiFi speakers800 that may be placed in a convenient location in the patient's home,apartment or related dwelling. Within the present context, terms such as“real-time”, “near real-time” or the like are meant to include thosewhere any delays in the processing and RF transmission of data aresubstantially imperceptible to a user. As such, delays that are measuredin no more than a few seconds are deemed to be real-time, whereas delaysthat are measured in minutes, hours or more would not be deemed to bereal-time. Likewise, the terms “real-time”, “near real-time” or the likewhen used in conjunction with the acquisition of one or more forms ofLEAP data are meant to distinguish such data from that acquired earlierfor baseline or historical purposes. As with the use of these terms inconjunction with the alerting and response times between a monitoredindividual and his or her caregiver as mentioned above, the meaning willbe apparent from the context.

Referring with particularity to FIG. 2I, compared to the stand-alone,wristband-based platform with the rigid structural housing 110 of FIGS.2A through 2H, FIG. 2I depicts a flexible platform 190 that can be worn,imbedded in or affixed to the patient's skin, clothing or accessoriessuch as belts, shoes, hats, eyeglasses or the like. For example, thewearable electronic device 100 can be configured as an adhesivebandage-like device to define a conformal skin patch or relatedsurface-level device. Within the present disclosure, a bandage orbandage-like flexible platform 190 or related configuration is meant toinclude all substantially conformal adhesive-based skin patches,regardless of how temporary the adhesion. For example, in one form, theadhesive properties may be such that when the wearable electronic device100 is embodied as a bandage, it is configured for short-term use, suchas over the course of a few hours or a day or two, while in anotherform, it may be configured for longer use, such as the duration of thelife of the battery 180, or even longer, if needed. Likewise, when thewearable electronic device 100 is embodied as a bandage, the variouselectronic modules, chips, systems and related components (such as thoseshown in FIG. 2F and that may include one or more of thepreviously-mentioned processor 173A, memory 173B, bus 173C, input/output173D and machine code 173E that in one form is stored on memory 173B),as well as a presently-shown display 173F, may be substantially encasedin order to give the wearable electronic device 100 water-proof (or atleast water-resistant) properties. Besides the wristband-based andbandage-based embodiments shown and described herein, it will beappreciated that other platform configurations (not shown) are withinthe scope of the present disclosure, including those that may befastened, clipped, pinned or otherwise secured indirectly to the patient(such as through an article of clothing) or otherwise secured directlyto the patient, the last of which may include a subcutaneous implant. Inthe latter form depicted in FIG. 2I, a resiliently-deformableskin-adaptive layer may be used for skin contact and adhesion, while oneor more layers may be formed on the skin-adaptive layer to form adata-collection circuit layer and one or more containment or packaginglayers. The circuit layer may be formed by known methods such astextiles, thin-film printing (including ink jet, flexography, screen,gravure or the like), and may include circuitry configured to includeone or more of an accelerometer, gyroscope, temperature sensor, strainmoisture sensor, acoustic sensor, inertial sensor, optical sensor,pressure sensor, chemical sensor or the like.

Electronically, the logic device 173 and the corresponding portion ofthe machine code may be configured to take context-based scenarios intoconsideration. In particular, a database of the acquired or baselinesignatures associated with a particular form of the LEAP data that isstored in memory 173B may be adjusted to take into consideration thelocation of the wearable electronic device 100 on an individual beingmonitored. As such, in situations where the wearable electronic device100 is affixed to an individual's wrist (such as when configured as awristband, watchband or the like), it may produce different activity,environmental or physiological data signatures than when embodied in abelt, hat, shoe or other article of clothing. Such context-specificinformation may be adjusted through manipulation of the data by aportion of the machine code in order to enhance data accuracy. It willbe appreciated that where the wearable electronic device 100 includessubcutaneous, on-skin or skin-attachable features (such as, but notlimited to, one or more of the sensors 121), the platform (andcomputational architecture) may be simplified to minimize complexity,reduce volume or weight or to offload some computational activities.

As can be seen from the configurations of both FIGS. 2H and 2I,improvements in the architecture of the wearable electronic device 100include a lightweight, small wrist-wearable or thin profile form factorthat permits unobtrusive addition to a person being monitored. Moreover,the use of a power supply in the form of a coin lithium battery 180combines long life with a thin profile that is easier to integrate intothe main housing assembly 110. In addition, low power supply consumptionis achieved by removal of a cellular, GSM, 3G, 4G (including LTE, LTE-Aand LTE—A Pro variants) or upcoming 5G connection and its emphasis onhigh-throughput machine-to-machine (M2M) rather than person-to-person(P2P) communication, as well as through controlling the wearableelectronic device 100 to be in a sleep mode during periods ofinactivity, as well as through the use of the aforementioned Class AALOHA-style communication for its LPWAN wireless communicationsub-module 175C. As previously mentioned, the wearable electronic device100 includes the third wireless communication sub-module 175C; in oneform, the wearable electronic device 100 may be configured to provideClass A, ALOHA-style asynchronous transmission that can supportbidirectional communication (although the latency and power requirementsmay differ depending on whether the wearable electronic device 100 is inan uplink mode or a downlink mode). In Class A operation, the wearableelectronic device 100 does not wait for a particular time with which tocommunicate with the gateway 300, but instead transmits when data isready to be sent. Such mode of communication is beneficial in that thewearable electronic device 100 in general and one or more of itswireless communication sub-modules 175A, 175B and 175C in particular mayremain in a so-called “sleep” (i.e., dormant) mode until needed, atwhich time it can be awoken; as such, an uplink message may be sent atany time, after which a pair of receive windows may be opened atspecified times such that the backhaul server 400 can respond to eitherwindow. This in turn helps to contribute to a low duty cycle for reducedbattery 180 power consumption. As discussed elsewhere in the presentdisclosure, so-called sensor events that may trigger an awakening fromthe sleep mode include a waking event or a set period of time, where theformer includes things such as movement that exceeds a threshold,receipt of an external control signal or the like.

Furthermore, long range transmitting in the wireless communicationsub-module 175C of the hybrid wireless communication module 175 allowsfor better system 1 data collection and associated backhaulfunctionality, where the range may be more than ten miles with directline-of-sight and over one mile in urban and related multi-bounceenvironments. In one form, the data that is received by the gateway 300uses frequency shift keying (FSK) via the CSS so that the data packetsreceived from the wearable electronic device 100 arrives in chirp-likedigital representation symbols that are subsequently parsed down to thefrequency domain and then modulated. In one form, the modulation ratemay be reduced while simultaneously keeping the same transmit power;this in turn promotes stronger transmitted signals from the thirdwireless communication sub-module 175C. The continuous interactionbetween the wearable electronic device 100 and the gateway 300 allowsthe data transmission to effectively hop to other frequency channels tomore efficiently take advantage of limitations imposed by power, speed,duty cycle and range of the wearable electronic device 100. In additionto efficiency improvements made possible by the FSK shifting of theinput signal frequency, adaptive data rate (ADR) may be used to have thewearable electronic device 100 communicate with the network server 410to boost the data rate through analysis of recently-sent messages todetermine if range and data transfer rates may be traded off against oneanother.

Furthermore, the hybrid architecture takes advantage of the low currentrequirements of the LPWAN mode of signal transmission, which in turnallows a more compact prismatic-shaped battery 180 (as shown) or a “coincell” battery (not shown) to be used rather than one or morevolumetrically inefficient batteries such as “AA” sized batteries. Inone form, the wearable electronic device 100 can selectively enable ordisable certain ones of the hybrid wireless communication sub-modules175A, 175B or 175C upon detection of certain forms of connectivitybetween the wearable electronic device 100 and the BLE beacons 200. Forexample, the sleep mode (or at least a reduced capacity mode) can beadopted to avoid using GNSS location sensing as long as lower-powermodes of communication (such as the BLE-based approach of the hybridwireless communication second sub-module 175B) are being used. In oneform, the wearable electronic device 100 has a battery 180 life of atleast about three days. In sleep mode, the hybrid wireless communicationmodule 175 may consume only several microamps of current, while whentransmitting or receiving only consuming no more than about 150milliamps, after which it returns to its sleep mode. Significantly, thisleads to an average current consumption of no more than about 1milliamp, depending on the broadcast interval and transmit powersetting.

Referring next to FIGS. 3A through 3K, a notional depiction of the useof a LoRa-based LPWAN to communicate between the wearable electronicdevice 100 and the gateway 300 and downstream system 1 components(including backhaul server 400 and cloud 500) to form an IoT device isshown for uplink (FIG. 3A) and downlink (FIG. 3B), as well as the dataformat packets (FIGS. 3C through 3K) for the various LPWAN-relatedcommunication functions of the wearable electronic device 100. As can beseen, in one form, much of the control activity may be conducted inother parts of the system 1, including the backhaul server or servers400, as well as with the cloud 500. Within the LoRaWAN™ address space, anumber of preprogrammed identifiers, keys or addresses with routinginformation may be used in order to establish communication between thewearable electronic device 100 and the gateway 300 and backhaul server400. As such, participation in a LoRaWAN™ network may achieve additionalsecurity by having each wearable electronic device 100 be personalizedand individually activated. In one form, this may be achieved in amulti-step process known as over-the-air activation (OTAA), where thewearable electronic device 100 must follow a key-based join procedureprior to participating in data exchange activity. For example, thewearable electronic device 100 may first be personalized with a deviceidentifier key DevEUI, an application identifier key AppEUI and a128-bit AES encryption key AppKey. Second, a pair of MAC messages areexchanged with the network server 410 in order to initiate a joinrequest and corresponding join accept. Third, the wearable electronicdevice 100 sends the join request message that includes the applicationand device identifier keys AppEUI and DevEUI of the wearable electronicdevice 100 followed by a random value DevNonce that is assigned to thewearable electronic device 100 in order to avoid replay attacks. Fourth,the join request message can be transmitted using a suitable data rateand frequency hopping sequence across the specified join channels.Fifth, the network server 410 will respond to the join request messagewith a join accept message. Sixth, after activation, the wearableelectronic device 100 is configured with three additional keys known asa device address key DevAddr, a network session key NwkSKey and anapplication session key AppSKey along with the application identifierkey AppEUI. The network session key NwkSKey for control and theapplication session key AppSKey for data act as security enhancementsthat are made known solely to both the wearable electronic device 100and the application server 420; these two keys allow the signature andsecure encrypted transmission of the data and information packets.Besides OTAA, activation may take place through a process known asactivation by personalization (ABP), although OTAA is often used becauseof its higher degree of security. In one form, the network session keyNwkSKey is used by both the network server 410 and the wearableelectronic device 100 to calculate and verify a message integrity code(MIC) of all data messages as a way to ensure data integrity. In oneform, the application session key AppSKey is used by both the networkserver 410 and the wearable electronic device 100 to encrypt and decryptthe payload field of the various application-specific data messages.

In one form, the device identifier key DevEUI is a 64 bit globalidentifier that is assigned to each wearable electronic device 100 bythe chip manufacturer. The device address key DevAddr is a 32 bitnon-unique device address assignment that is responsible for allcommunication between the wearable electronic device 100 and theapplication server 420 where some of the 32 bits are fixed for aparticular network (for example, an open IoT LoRaWAN™ device such as theThings Network or the like) and the remainder of the bits can beassigned to individual ones of the wearable electronic devices 100through either the OTAA or ABP activation approaches mentionedpreviously. The application identifier key AppEUI is a 64 bit globalidentifier that addresses space and uniquely identifies the applicationprovider (for example, the owner of an assisted living facility or—inthe case of an installation in a private residence—the wearableelectronic device 100 manufacturer or retailer) of the wearableelectronic device 100 such that when an application is created, thenetwork server 410 allocates the application identifier key AppEUI froma predefined address block. In one form, the application identifier keyAppEUI is used along with the device identifier key DevEUI to derive thenetwork session key NwkSKey, application session key AppSKey and thedevice address key DevAddr. As with the device identifier key DevEUI, acomparable key known as the gateway identifier key GatewayEUI may beimplemented in a similar way (along with the equivalent keys AppKey andAppEUI) in order to register the gateway 300 on the particular network.As with the application identifier key AppEUI, the gateway identifierkey GatewayEUI is a 64 bit unique and embedded identifier, nowspecifically for the gateway 300. Some of these keys may be depicted asmessage headers that may be used by various APIs of certain devices thatare either used in conjunction with the wearable electronic device 100.For example, the LoRaWAN™ message header “uint64 t” that corresponds tothe device identifier key DevEUI or the “uint32_t” that corresponds tothe device address key DevAddr may be used to identify which wearableelectronic device 100 selected from numerous wearable electronic devices100 the message was received from. Keys and message headers such asthese are helpful in situations, such as an assisted living community orother multi-person dwelling, as a way to more particularly identifyacquired data with a corresponding individual.

Various data formats (also referred to as packet structure byte maps ina manner similar to a memory map or related data structure) are shownfor communication of information between the wearable electronic device100 and the system 1. These data formats include BLE beacon 200 dataformat (FIG. 3C), GNSS 10 data format (FIG. 3D), battery 180 data format(FIG. 3E), nurse call button 131 data format (FIG. 3F), nurse responsedata format (FIG. 3G), band worn data format (FIG. 3H), band removeddata format (FIG. 3I), no movement data format (FIG. 3J) and movementdata format (FIG. 3K) all of which are shown within each eleven bytedata packet framework. These packets of data may include the controlportion and a payload (or user) portion such that when sent over apacket switching or a related network, the data placed in the header(i.e., the control portion) may be used to direct the packet to itsinternet or cloud 500 (or other system 1) destination and perform errordetection to allow substantive operations on the payload portion of thedata. In one form, Internet Protocol version 6 (IPv6) and its 128-bitaddress may be used to locate and identify various computers on one ormore networks, as well as to direct data traffic over internet or cloud500.

In one form, the system 1 achieves wireless communications between thewearable electronic device 100 and the application server 420 using theLoRaWAN™ version of the LPWAN specification. The wearable electronicdevice 100 operates as a Class “A” device using a Data Rate 0 (zero),although it will be appreciated that the other data rates (such as thosedisclosed herein) may also be used. The LoRaWAN™ specification has anumber of defined fields in the message headers that are used by theapplication. For example, the LoRaWAN™ port field may be used toidentify the type of message that is being received by the applicationserver 420, while the LoRaWAN™ payload field contains the message data.In one form, various messages are supported, including thepreviously-discussed data of FIGS. 3C through 3K each of which will bediscussed in more detail as follows.

Referring with particularity to FIG. 3C, the LoRaWAN™ beacon 200 dataformat is shown, and will have a port number of 0x01, as well ascorresponding from one to three BLE beacons 200 that are currentlyvisible to the wearable electronic device 100; the message correspondingto this portion of the data format will be issued by the wearableelectronic device 100 as needed. The BLE beacon 200 entries will befilled in in the order of 1, 2 then 3. Unused entries will have astrength of 0xff, while the message port is 0x01. The number of secondsthe message may be held before being sent to the LoRa portion of thehybrid wireless communication module 175 (specifically, the thirdwireless communication sub-module 175C) module is referred to as thedelay, where 255 is the maximum value as measured in seconds. The UUIDidentifiers may be used for each BLE beacon 200, presently representedby uuid1, uuid2 and uuid3. Depending on the choice of firmware module,either the actual RSSI is reported, or the estimated distance isreported from each BLE beacon 200 to the wearable electronic device 100.In situations involving the former, received signal strength (such asRSSI-measured signals) are represented by strength1, strength2 andstrength3, where the higher the number is correlated to the wearableelectronic device 100 being farther away from the BLE beacon 200. Anentry of 0xFF specifies a blank beacon entry in the message, while anentry of 0xFE represents the maximum value of 254 or more. In one form,the defined flags values for a particular message may be set as shown inTABLE 1 to determine which of the BLE beacons 200 are configured aselopement beacons 200B.

TABLE 1 Flag Value Associated BLE Elopement Beacon 0 None 4 Beacon 1 2Beacon 2 1 Beacon 3 6 Beacons 1 and 2 5 Beacons 1 and 3 3 Beacons 2 and3 7 All

Referring with particularity to FIG. 3D, the LoRaWAN™ GNSS 10 dataformat is shown. The message will have a port number of 0x02, and willcontain the location of the wearable electronic device 100 relative to aGNSS 10 fix. This message will be issued by the wearable electronicdevice 100 as needed. The message port will have the 0x02 entry. Thedelay is the part of the message similar to that as previously describedin the LoRaWAN™ beacon 200 data format of FIG. 3C. Thelatitude/longitude entry corresponds to the coordinates of the GNSS 10fix. The data is encoded as a 32 bit signed integer; for example:0xFAC159DA=−87.991846. The accuracy corresponds to the number (inmeters) for which the wearable electronic device 100 enjoys at least a90% confidence level of its location. For the fix, the NMEA GGA “fixquality” field is passed from the GNSS 10 unit. The NMEA defined valuesare: 0x00=invalid, 0x01=GNSS 10 fix (SPS), 0x02=DGNSS fix, 0x03=PPS fix,0x04=real-time kinematic, 0x05=float RTK, 0x06=estimated (deadreckoning) (2.3 feature), 0x07=manual input mode, 0x08=simulation mode.For the wearable electronic device 100, the following codes have beenadded by the management software used for GNSS 10. 0xFE means that theGNSS 10 did not respond to message after communications was established,and 0xFF means that the GNSS 10 did not respond to the first message.

Referring with particularity to FIG. 3E, the LoRaWAN™ battery 180 dataformat port is shown, where 0x04 is the port number, while the delay isthe part of the message similar to that as previously described in theLoRaWAN™ beacon 200 data message format of FIG. 3C. The battery chargestatus is shown, where values 0x00 to 0x64 correspond to the percentageof battery life remaining.

Referring with particularity to FIG. 3F, the LoRaWAN™ nurse call button131 data format message will have a port number of 0x05 that is issuedwhen the user presses the nurse call symbol 131 that is formed on thecover 130 of the wearable electronic device 100. As before, the delay isthe part of the message similar to that as previously described.

Referring with particularity to FIG. 3G, the LoRaWAN™ nurse responsedata format message will have a port number of 0x06 that is issued whenthe nurse call status of the wearable electronic device 100 is cleared.The delay is the part of the message similar to that as previouslydescribed in conjunction with FIGS. 3E and 3F. The UUID is the uniqueidentifier of the closest nurse beacon or tag at the time the nurse callstatus was cleared. Likewise, the strength of the closest nurse beaconor tag is included in the message. This field is in the same units asthe beacon data message.

Referring with particularity to FIG. 3H, the LoRaWAN™ band 190 worn dataformat message will have a port number of 0x07 that is issued when thewearable electronic device 100 detects that it is being worn. The delayis the part of the message similar to that as previously described inconjunction with FIGS. 3E, 3F and 3G.

Referring with particularity to FIG. 3I, the LoRaWAN™ band 190 removed(i.e., not worn) data format message will have a port number of 0x08that is issued when the wearable electronic device 100 detects that ishas been removed. The delay is the part of the message similar to thatas previously described in conjunction with FIGS. 3E through 3H.

Referring with particularity to FIG. 3J, the LoRaWAN™ no movement dataformat message will have a port number of 0x09 that is issued when theuser has been stationary for a threshold amount of time. The delay(measure as the number of seconds the message was held before it wassent to the LoRa module) may be set with a maximum value of 255, whichmeans more than 4 minutes and 24 seconds. Stationary time is the numberof seconds the user has been stationary. This field will report 65535for times longer than 65535 seconds.

Referring with particularity to FIG. 3K, the LoRaWAN™ movement dataformat message will have a port number of 0x0A that is issued when theuser begins moving after having been stationary. As before, the delay isthe number of seconds that the message was held before it was sent tothe LoRa module.

Referring again to FIGS. 1, 2F, 3A and 3B, unlike relying upontraditional WiFi or BLE to transmit the collected LEAP data, wherenumerous routers or hubs would have to be deployed in an ad hoc meshtopology through repeaters or related devices to pass information fromone device to another in order to ensure signal reception over theentirety of an assisted living community facility or multi-room privatedwelling, the LPWAN-based approach disclosed herein allows coverage ofthe entire facility with a smaller number of gateways 300 in a startopology with long-range connectivity such that a wireless networkformed between the wearable electronic device 100 and the gateway 300has sufficient redundancy to ensure that data signals being transmittedfrom the wearable electronic device 100 arrive at the backhaul server400, regardless of the failure of a single gateway 300 or other wearableelectronic devices 100 that may also be signally cooperating with one ormore gateways 300. As such, overall installation architecture isimproved by avoiding complexity, as well as eliminating the need forhaving only AC-powered devices or nodes be capable of receiving andtransmitting data and other information between nodes. Significantly,using a single gateway 300 (or no more than a small number for verylarge facilities and system 1 deployments) can reach the backhaul server400 with minimum amount of installation time and expense, as well as no(or limited) need to integrate into existing WiFi infrastructure thatmay already be present within the facility. Gateways 300 transfer areceived packet from the wearable electronic device 100 to thecloud-based network server 410 via some additional backhaul (such ascellular, ethernet, satellite or WiFi), and can do so through any othergateway 300 (if needed) that is within signal range, making the wearableelectronic device 100 and its LPWAN-based transmission approachdisclosed herein easier to pair the to the cloud 500. Gateway 300 (likeall radio systems) cannot hear anything while it is transmitting; assuch, the bidirectional communication capability of LPWAN is primarilyused as an uplink-based network such that the wearable electronic device100 is acting as an end-node other than times where it initiates thecommunication. In addition, the scalability of the LPWAN-based approachallows a system operator, developer or installer to deploy as manygateways 300 as needed without concern for overloading the network.

Similarly, using an LPWAN-based approach to transmit from the wearableelectronic device 100 to one or more gateways 300 avoids the cost andcomplexity of cellular-based approaches such as those associated withexisting machine type communications (eMTC, such as LTE-Cat M1) ornarrowband IoT (NB-IoT, such as LTE Cat-NB1) technologies that rely uponlicensed portions of the spectrum and non-mobility of the end nodedevice, as well as the imminent 5G massive machine type communications(mMTC). Likewise, unlike relying upon cellular towers and relatedinfrastructure to transmit the collected LEAP data, gateway 300 employsan agnostic (that is to say, stateless) protocol where neither receiptacknowledgement nor information is retained by the transmitting orreceiving devices. In addition to providing enhanced data security, sucha protocol simplifies system 1 architecture in that memory allocation ofthe transmitted and received data need not be done dynamically duringthe duration of the communication. Moreover, neither the wearableelectronic device 100 nor the gateway 300 have to establish a session tocommunicate, instead having some of the more complex operations moveddownstream, such as to the backhaul server 400 and its cloud-basednetwork server 410. This allows simplification of the gateways 300 thatin turn reduces the costs associated with the deployment of system 1.

Furthermore, the star topology between the wearable electronic devices100 and the various gateways 300 avoids the need to use a wirelessneighborhood area network (WNAN) or crowdsearching GNSS-based protocolboth of which rely on an associated mesh-like network of adjacent mobiletelephones or wireless broadcasters that in turn depend upon interphonenode-to-node communication with which to achieve their range-extendingalong with attendant increases in network complexity and decreases inbattery life and network capacity. The spoke-hub distribution of thestar network topology ensures wide area coverage for patient locationwithout the mesh network multi-hop routing drawbacks. In particular, theapproach employed by the wearable electronic devices 100 and the variousgateways 300 of the present disclosure differs significantly fromcrowdsearching GNSS-based methods or WNAN methods in that there is norequirement to have strangers share their mobile telephone orbroadcaster resources in order to have the patient location data sentfrom such stranger devices to the caregiver, family member or otherresponsible party, nor is there a need to repeatedly send a UUID orrelated identifier to nearby ones of these stranger devices in order tohave them communicate with a main server to update location informationof the patient. Not only does the central management of thecommunication between the wearable electronic devices 100 and thevarious gateways 300 of the architecture disclosed herein promotelong-range, simple connection infrastructure, but it also providesenhanced data security, which may be valuable in situations wheresensitive information pertaining to the patient is being transmitted.Furthermore, using the third wireless communication sub-module 175Calong with the gateway 300 in a star topology network rather than a meshtopology network avoids the need for maintaining the hybrid wirelesscommunication module 175 of each wearable electronic device 100 as arelay or source for signals from other similarly-equipped wearableelectronic devices 100 which in turn means that the hybrid wirelesscommunication module 175 need not remain in an active communication modewith consequent increases in battery 180 power consumption.

Referring next to FIG. 4, the configuration application is used toactivate the wearable electronic device 100, associate the wearableelectronic device 100 with a patient P (or person P) to whom the deviceis attached, manage BLE beacons 200 and perform software updates ofwearable electronic device 100 firmware. In particular, theconfiguration application can be made to run on a client device in theform of the previously-mentioned remote computing device 900 that can beused by family, friends, nurses, doctors or other interested caregiversC. In one form, the remote computing device 900 may be a caregiver Cworkstation, mobile telephone, smartwatch, tablet computer or otherdevice capable of receiving location, status updates, alerts or relatedinformation about an individual associated with the wearable electronicdevice 100, as well as conveying such information to the correspondingcaregiver C. In use that takes place during initial pairing or setup,the communication for the configuration application may be establishedthrough BLE-equipped connectivity features as long as the wearableelectronic device 100 and remote computing device 900 are withinsuitable range of one another. In one form, the registration of thewearable electronic device 100 with one or more BLE beacons 200 isperformed automatically through the first wireless communicationsub-module 175A of FIG. 2F upon determination by the wearable electronicdevice 100 that it is within range of a UUID-bearing signal beingtransmitted by at least one the BLE beacons 200. Likewise, suchregistration may be performed semi-automatically through an applicationinitiated by a caregiver C or other user from the remote computingdevice 900. In either event, the configuration application allows apatient P or caregiver C to create an account for the patient P, as wellas to associate the wearable electronic device 100 with that accountusing the UUID. In one form, the configuration application may be usedto establish a system maintenance view that provides a display orrelated indicia to show how other devices (including those within system1) may interface with the wearable electronic device 100 in order tofacilitate such configuration, as well as to provide operability statusof the various components and sub-components, as well as of the wearableelectronic device 100 for—among others—a charging status of the battery180. In one form (such as that associated with a mobile telephone,smartwatch or the like), the communication used in conjunction with theconfiguration application may be established through Bluetooth-equippedconnectivity features, while in another (such as that associated withlaptop computers or other hardwired machinery) the communication may beestablished through landline-equipped internet connectivity featuressuch as a web browser or the like. Various setups and configurations arepossible, including (1) over-the-air firmware updates, (2) initialnetwork settings (such as initial GNSS seed location of the facilitywhere the wearable electronic device 100 will be deployed), (3)off-network (i.e., a setting to allow a wearer to leave the facilityunder supervised conditions such as with family or friends), (4) BLEbeacon 200 scanning and event transmission rules, (5) as well as otherconfiguration data to be determined.

Referring next to FIG. 5, when the caregiver application or familyapplication is loaded on a suitably-configured remote computing device900, various events may be tracked, including (1) location changerelative to a BLE beacon 200 position that is closest to the wearableelectronic device 100, (2) change in position relative to GNSS 10position, (3) battery 180 remaining life, (4) nurse call and subsequentresponse, (6) placement on or removal from a patient P of the wearableelectronic device 100 and (7) patient P movement or lack thereof. In oneform, and upon receipt of a suitable signal from the wearable electronicdevice 100 through gateway 300 (neither of which is presently shown inFIG. 5), the various different remote computing devices 900 may be madeto interact with one or both of the application server 420 and cloud 500in order to be notified of events or to check on a person associatedwith the wearable electronic device 100, such as over an internetprotocol (IP), cellular network or the like. Thus, in one form, thewearable electronic device 100 may form a front-end portion of acloud-based full-stack IoT configuration while the remote computingdevices 900 may form the corresponding back-end which may also collectand store large amounts of so-called “big data” from the wearableelectronic device 100 to provide the detection and associated analyticalactivities discussed herein. It will be appreciated that cloud 500 maybe used in conjunction with closed-source or open-source databasesoftware and commercially-available large-scale processing services toachieve some or all of its functionality.

As previously mentioned, in one form, the identifier being sent from thenurse ID beacon 200C may include code that particularly identifies thestaff member such that this code and the particular staff member'sresponse to a patient P request from the nurse call button 131 is sentto the application server 420 or cloud 500. This identifier may belogged to allow the system 1 to keep track of information including whena person P (that is to say, patient P) calls for help, how long it tookfor help to arrive, and particular identification of which staff memberor other caregiver C provided the help to the patient P. In addition,caregivers C and other personnel within a multi-resident dwelling (suchas the previously-mentioned assisted living facility) will have theability to monitor residents from a centralized location, which in turnincreases the regularity and efficiency of checking in on the residents.In addition, this will help simplify and make more efficient the passingof medication in that rather than searching endlessly for peripateticresidents, the location-tracking feature provides real-time informationon resident whereabouts. Moreover, with the gyroscopes, accelerometers,impact sensors and other forms of sensors 121, fall detection alerts arealso conveyed automatically and in real-time, which is particularlybeneficial in situations where the fallen resident is out of reach anemergency cord. This automation ensures that a suitable alert is sent,regardless of whether the nurse call button 131 is engaged by the fallenresident. Furthermore, because the nurse ID beacon 200C uses BLE tobroadcast and track the whereabouts of caregiver C, detection rangessuitable for a home or an assisted living community-sized facility areeasily achieved without having to install large numbers of repeaters,routers or hubs. In one form, the transmission of alerts from thewearable electronic device 100 may be sequenced or prioritized based onthe relative importance of the type of caregiver C to the individualthat is sending the alert, by proximity of the various caregivers C tothe wearable electronic device 100, or by some other approach.

It will be appreciated that when BLE beacons 200 are placed in one ormore rooms such as one of living room LR and BR (both as shown in FIG.9) as well other rooms (such as a kitchen, bedroom or the like, none ofwhich are shown), the UUID of the BLE beacon 200 must be associated witha unique one of these locations, and that this association must bestored in a database, such as within the backhaul server 400. Likewise,the one or more sensors 121 of a specific wearable electronic device 100may need to be registered. As such, in one form, a unique identifier fora particular one of the sensors 121 must be associated with a locationand a specific function (such as activity, physiological aspect or thelike); this associated data must also be stored in a database (again, inone form in the backhaul server 400). In doing so, such sensor 121 willprovide data for only a specific patient that is associated with thatparticular wearable electronic device 100. In one form, suchregistration may be performed manually, while in another, in anautomated fashion. Furthermore, the registration process may beperformed with a mobile device (such as the remote computing device 900)that has been equipped with a registration application in a mannersimilar to the applications depicted in FIGS. 4 and 5.

II. Machine Learning for Analysis of Wearable Electronic Device Data

Regardless of where the computational activities used to operate uponthe LEAP data take place (that is to say, either locally in an edgecomputing manner on the wearable electronic device 100, on the gateway300 or remotely within the backhaul server 400, cloud 500 or othercomponent cooperative with or within system 1), such activities mayemploy machine learning approaches that synthesize rules solely from theacquired data rather than through the explicit programming instructionsof an a priori rules-based protocol. Within the present context, machinelearning is understood to include the use of one or more algorithms toextract information from raw data and represent it in some type ofmodel. In one form, the acquisition and extraction of the data goesthrough a training process with the algorithms as part of a n orderedworkflow that will be discussed in more detail as follows. As part ofthis workflow, the resulting model may then be used to infer thingsabout other data that has yet to be modeled. More particularly, such amachine learning-based analysis of the LEAP data may convert such datainto clinically-relevant predictions with which to help caregivers Cdetermine whether a person P to whom the wearable electronic device 100is attached is at risk of an adverse health condition. In this way,rather than start with an expert-based predetermined logic pertaining toa particular health issue (such as UTIs, pneumonia, variousneuropsychiatric conditions or the like) and then applying the acquireddata to such logic, the approach of the present disclosure may employ anobservation-based or example-based way to create new forms ofprobabilistic health diagnosis logic. This in turn provides a method ofperforming analysis of the data collected by the wearable electronicdevice 100 in order to automate the building of data-driven clinicaldecision-making models with limited human intervention.

Examples of machine learning include those grouped as supervisedlearning, unsupervised learning and reinforcement learning, and one ormore approaches under these groups may be useful for such analysis ofthe acquired data. Particular examples of supervised learning mayinclude Bayesian approaches (such as naive Bayes, Bayesian belief,Bayesian linear regression and dynamic Bayesian networks that includesMarkov-based models), decision tree approaches (such as classificationand regression trees (CART) or C4.5), ensemble approaches (such asrandom forests, boosting and bootstrap aggregating (bagging)),instance-based approaches (such as k-nearest neighbor (kNN)), regressionapproaches (such as linear regression, least-squares regression or thelike), support vector machine (SVM) approaches and some forms of deeplearning (particularly when used as a classifier, such as with aconvolutional neural network (CNN)). Particular examples of unsupervisedlearning may include clustering approaches (including K-means,k-medians, expectation maximization, hierarchical, density-based or thelike), dimensionality reduction approaches (including principalcomponent analysis (PCA), linear discriminant analysis (LDA) or thelike), and at least some form of neural networks with their acyclicconnected graph (ACG) feedforward, and recurrent variants which may befurther grouped into, among others, perceptrons, sequential/recurrent,long short-term memory (LSTM), Hopfield, Boltzmann machines, deep beliefnetworks, auto-encoders or the like. Particular examples ofreinforcement learning (which in some forms is a variant of supervisedlearning) may include Q-Learning, Deep Q-Learning, Actor Critic, PPO,Policy Gradients or the like, where maximum expected outcomes aregrouped as either value-based, policy-based or model-based approaches.It will be appreciated that the use of a particular machine learningapproach such as the ones discussed herein is dictated by numerousfactors including the type of data (for example, the size and structureof such data) being acquired, what subsequent analysis of the data isbeing required, the availability of computational time and how soonresults are needed. Moreover, it will be appreciated that hybridapproaches of more than one of the machine learning models discussedherein may be employed in order to infer changes in one or both ofphysical and mental status of an individual from whom data is beinggathered by the wearable electronic device 100, and that all suchvariants of such models are deemed to be within the scope of the presentdisclosure. As such, descriptions of analyses as being performed on theacquired LEAP data by, substantially by or based on a particular machinelearning model within the present disclosure will be understood toinclude such models or hybrids regardless of minor variations in theirfunctionality so long as their underlying functionality is preserved.For example, a K-means clustering-based model will be understood topossibly include variations on its functionality of using a series ofiterations to create clusters of the acquired LEAP data into data pointsthat have similar variance and minimized cost function without a loss ingenerality or applicability of the model to the underlying analysis ofthe LEAP data.

In addition to grouping machine learning models based on whether theyare supervised or unsupervised, they may be grouped according to theiroutput, where in one form, a binary classification model provides ayes/no answer, whereas a regression model provides an answer that existsalong a continuum of answers. Examples of classification models includeSVM, kNN, decision trees, Naïve Bayes, logistic regression and randomforests, among others, while examples of regression models includelinear regression and nonlinear regression.

Some of the machine learning approaches that may be relevant to theproblem of predicting potential health risks of a person P using thewearable electronic device 100 will now be discussed in more detail,particularly as they relate to the ability of one or both of thewearable electronic device 100 and system 100 (possibly working inconjunction with the gateway 300, the cloud 500 or other equipment) tostatistically model the health of a person P who is wearing the wearableelectronic device 100 with as much predictive accuracy as possible withthe minimum amount of machine learning model resources as possible. Inone form, the machine learning model used to predict whether aparticular person P is manifesting signs of infections such as a UTI,respiratory tract infection (RTI), skin and soft tissue infection (SSTI)or gastrointestinal tract infection (GII), mental or cognitive changes,respiratory problems or the like can take into consideration numerousfactors in order to enhance its utility. For example, the relativeimportance between model accuracy, stability, predictiveinterpretability and simplicity may be adjusted in order to meet aparticular end-use objective. In some cases (for example, low-bias caseswhere the likelihood of a health condition or other outcome beingpredicted is relatively balanced such that the data set includes indiciawhere a “yes” answer is about the same as a “no” answer in aclassification-based model), accuracy as a performance metric may beimportant, whereas in circumstances where the likelihood is imbalanced,accuracy may not be as important of a metric as the previously-mentionedsensitivity, specificity, receiver operating characteristic (ROC) andarea under the ROC curve (AUC) or the like. Within the present context,a balanced likelihood is one where when both possible outcomes arerelatively equal.

Bayesian-based statistical inference includes supervised learningapproaches that are useful to analyze distributions in acquired data notjust to represent the distribution values, but also to determine thebelief (that is to say, the probability, or likelihood) that each one isa true value, as well as a probability that to-be acquired data willindicate changes in the object from which the data was acquired. In oneform, Bayesian-based approaches form probabilistic graphical models thatin turn can provide classification guidance data based on such beliefsor likelihoods. For example, a Bayesian-based approach applied to thecollection of data from the wearable electronic device 100 may look forcorrelations between location, activity or other data and a particularmedical condition. Because Bayesian-based approaches consume relativelysmall amounts of processing capability, the resulting inference can beperformed locally (such as on the wearable electronic device 100)without the need to first convey large quantities of the LEAP data tothe backhaul server 400 or other parts of system 1. Bayesian networksgenerally perform well on data with large amounts of samples, as well asfor temporal (i.e., time series) data such as electronic medical records(EMRs). Bayesian-based approaches are probabilistic by nature, such thatonce symptom-based data is presented to the model, it can be used tocompute the probabilities of imminent adverse health conditions of theperson P from whom the wearable electronic device 100 is acquiring data.Moreover, Bayesian-based approaches are well-suited to showing eventcausation through their modeling of conditional dependence. Depending onthe nature of the data being acquired, Bayesian-based approaches mayexhibit similar predictive its performance to neural network-based anddecision tree-based approaches. Tracking patient activity using thesensors 121 that are present within the wearable electronic device 100include those approaches for gaining insights into static activity(using, for example, Naive Bayes for text analysis, kNN, SVM, decisiontrees or the like), and temporal activity (using, for example, DynamicBayesian, Hidden Markov Model (HMM) and Conditional Random Field (CRF)probabilistic modeling approaches). In one form, the Bayesian approachallows a user to encode one or more prior beliefs about how the modelshould look irrespective of what the data indicates, especially insituations where the amount of acquired data from the wearableelectronic device 100 may be limited. Thus, Bayesian-based techniquesmay be used to conduct HAR and ADL studies on an individual person Pbasis, as well as to infer changes in the health of such person P, usingthe wearable electronic device 100, either in conjunction with theresults of the HAR and ADL studies, or directly from the data itselfwithout regard to such HAR and ADL information; as will be discussed inmore detail later. In such case, the Bayesian model may be used to forma target (i.e., response) variable of the desired corresponding HAR, ADLor change in health status. Such a model may be supervised in thattraining data with known targets that are correlated to one or morehealth conditions may be used during training to model to learn topredict such change in health from HAR or ADL that in turn is based onthe other variables that are in the form of the data being acquired bythe wearable electronic device 100. Likewise, such a model may beunsupervised in that K-means clustering or other approaches are used,where only limited size data sets are available. In this way,unsupervised learning can be viewed colloquially as finding patterns orstructure in the acquired data without understanding it. Moreover,depending on the desired output, the HAR, ADL or change in health maydetermine if the model is a classification one or a regression one. Forexample, certain changes in health status—such as periodic outbursts bya person P exhibiting one or more neuropsychiatric conditions—may beseen as existing over a continuum (making it a regression problem),while distinguishing between other types of changes could be construedas a classification problem.

A Markov model is one form of a memoryless Bayesian network model usedfor event recognition by ensuring that events that occurred before acurrent state do not influence the current state; in this way, futurestates only depend only on the current state. In other words, theMarkovian process assumes that if the present state of a measuredcondition is known, the future state is independent of the past state.As such, Markov-based approaches form a comparatively simple way tomodel random temporal-based sequences such as those associated withmotion-based activity data that can be acquired by activity sensors121B, possibly in conjunction with location data as acquired by thehybrid wireless communication module 175. Markov model analysis oftemporal sequences is particularly useful in situations where a largeamount of data is available in order to recognize complex temporalevents, and further where an ample amount of training to ensure asuitable tradeoff between bias and variance (in a manner similar to theuse of error and regularization terms to minimize cost function) ispresent. One specific variant, HMM, is an adaptive (that is to say,feedback-based), generative, probabilistic approach in which the systembeing modeled is assumed to be a Markov process with unobserved (i.e.hidden) states. With an HMM, knowledge of the correlation between aphysical event and a particular state is not as important as matchingeach state to a given output as a way to observe the output over time todetermine the sequence of such states. Thus, an HMI facilitates themodeling of a given event or process with a hidden state that is basedon observable parameters, particularly in determining the likelihood ofa given sequence. Such a framework is particularly useful for modelingevents that have temporal-based data structures (such as that associatedwith movement or positional data that is acquired from accelerometers orgyroscopes, as well as speech recognition, speech generation and humangesture recognition) in that an HMI can be visualized as essentially aquantization of a system's spatial components into a small number ofdiscrete states, together with probabilities for the time-basedtransitions between such states. By way of example, a multi-roomdwelling such as a house, assisted living facility or the like may bemodeled with a Markov-based approach such that one or more of variousrooms or spaces (for example, a kitchen, a dining room, a bedroom, abathroom, a hallway or the like) may be outfitted with one or more BLEbeacons 200. By collecting and conveying location data about a wearerwithin such a dwelling, the movement, activity and location-sensingcomponents within the wearable electronic device 100 may be used inconjunction with HMM to make a series of predictions about which roomthe wearer may enter next, based at least in part on sets ofprobabilities for each room. By performing such an analysis over largenumber of iterations (including temporal-based iterations), the HMMmodel may be able to improve the accuracy of predicting which room thewearer is likely to occupy next in order to form one or both of baselinewearer state of health and changes to such health.

Decision tree-based learning is supervised learning that uses ahierarchical disjunctive normal form of logic for either recursiveclassification or regression problems to maximize information gain (thatis to say, reduction in calculated variable entropy). In situationswhere the target variable can take a discrete or categorical set ofvalues, the learning is called a classification tree, where each pointin the data is represented as an internal node, each so-called branchrepresents the outcome of a test, and the so-called leaves represent theresulting classification. In one form, each decision tree within themodel is created using a different, random subset of so-calledweak-learner attributes and observations from the original training dataset such that they can work in conjunction with one another to become astronger algorithm. In one form, decision tree classification may beused in medical diagnosis and disease identification, where the outputof a classification tree is a class (“yes” or “no”), such as answeringthe question “will a patient have a heightened risk of contracting aUTI?” Likewise, in situations where the target variable can takecontinuous values, it is called a regression tree an example of whichcould involve predicting how long will a patient's length of stay be ina hospital. Decision trees may be formed over numerous parametriciterations, using factor-based analyses and related approaches to removeextraneous or duplicative data. In one form, such as CART, decisiontrees can be used to form output variables that are extremelyhomogeneous, thereby allowing subsets of data (for example, whether ademographic group used as part of a baseline set of data forinter-patient baselines has a particular condition that may be used toidentify potential co-morbidities in a patient being monitored with thewearable electronic device). This in turn may form the basis for ananalysis to determine what combination of sensed parameters may best beindicative of an emerging adverse health condition such that one or bothof additional analysis and intervention by a physician is warranted.Decision tree models may be interpreted by humans, as opposed to neuralnetworks and other black box-based models that provide no insight intohow the model was derived. As such, decision trees may be configuredsuch that a particular set of training data can be described in a way tobetter understand the relationship between the input variables that areused to form a predictive analysis. Decision trees may tend to producehigh-variance, low-bias results. Decision trees may be used for complexforms of data, such as those acquired from accelerometer-based sensors121, as the model does not assume a simple parametric relationshipbetween the accelerometer counts and the measurable indicia of apatient's activity, which in turn allows the model the flexibility todetermine which parts of the acquired acceleration signal may be moreprobative of the patient's actual activity. Decision tree-basedapproaches tend to work well with relatively small data samples.

Logistic regression-based learning is a popular supervised learningmodel that (despite its name) is used for binary classification topredict a specific discrete outcome where a qualitative rather thanquantitative response variable is produced, an example of which may bethat a given person P has a set of symptoms, is it possible to attributesuch symptoms to one of numerous possible medical conditions. Its use ofthe sigmoid-based activation function allows any real-valued inputnumber to be mapped into an output value between 0 and 1 such that itcan be transformed into one or the other based on a thresholdclassifier. Logistic regression does not require a lot of computationalresources, which makes it easier to implement on the wearable electronicdevice 100 where processor power may be less than that of the server 400or cloud 500. In addition, training a logistic regression algorithm inorder to arrive at a predictive health condition model is relativelyeasy. As with decision trees, logistic regression-based approaches tendto work well with relatively small data samples. In addition, they tendto be most useful when used in binary (that is to say, “yes/no”)classification problems. On the other hand, if continuous rather thancategorical outcomes are desired, logistic regression may be limited inits predictive ability. It may also be necessary to identify all of theimportant independent variable features that mathematically describe theinstances with a suitable label, as well as perform some form of featureengineering such as feature extraction in order to reducehighly-correlated duplicate data, as well as to gain some insights fromthe raw LEAP data form which a meaningful feature may be formed.

Ensemble-based approaches may aggregate other learning models as a wayto improve predictive performance. In one form, the ensemble may performbootstrap approaches, including bootstrap aggregating (that is to say,bagging) as a way to estimate a quantity from a data sample by usingindependently-trained subsets of the data to separate more importantpredictors from less important predictors. Random forest is one type ofsupervised ensemble model that can be used for both classification andregression problems, and in one classification form may be constructedas an ensemble of numerous decision trees where each of the decisiontrees is based on a subset of attributes and observations from anoriginal data set. After each decision tree is “grown” using a trainingdata set and applied to a test data set, a resulting classification isreturned that best matches the classifications provided by the largestnumber of individual decision trees. Ensemble-based approaches may alsoemploy boosting-based approaches for classification problems through theweight-adjusted iterative addition of single-level decision trees (andrelated weak-learning models) so that the resulting classifier isstrengthened. In this way, boosting, bagging and related ensemble-basedapproaches may be useful to determine a particular adverse healthcondition, such as when a person P associated with the wearableelectronic device 100 is at greater risk of contracting a UTI. In oneform, an ensemble of other models (such as those based on neuralnetworks) may be used in order to increase the accuracy of dataanalytic-based predictions of UTIs or other adverse health conditions.Some ensemble-based approaches such as the random forest functions wellwith small data sets and—like a Naïve Bayes approach—can performmulticlass classification functions.

Instance-based learning approaches construct hypotheses directly fromthe input/output training data pairs themselves without the need forseparate training. This can be advantageous in that it permits the modelto adapt to new data, as well as to ignore data that may no longer berelevant, which in turn permits the hypothesis to grow in complexity asthe amount or nature of the acquired data grows. Instance-basedapproaches may also keep the amount of data (and the concomitant amountof memory requirements and overfitting risks) tractable using one ormore instance reduction models. Thus, in situations where the acquireddata possesses many features, memory and related storage may become anissue such that the instance-reduction algorithms may help reduce theneed to keep an entire training data set in memory. Contrarily, whenusing one popular form of instance-based learning (in particular, kNN),it may be advantageous to either operate on smaller data-sets or employan instance-reduction algorithm as part of a multilayered analysis wherea disease or other medical condition is first classified based on theacquired LEAP data and then detected for its actual presence using someor all of the same LEAP data. For example, a process may use kNN for theinitial classification to identify a disease subset (also known as adiagnosis class) to which a particular disease or medical conditionbelongs based on commonality of symptoms, after which other classifieralgorithms as discussed herein that are trained, validated and tested onthe LEAP data perform detection-specific analyses of the likelihood of aparticular disease using the symptom information inferred by theacquired data from the wearable electronic device 100. In other words,kNN takes into consideration one or more nearest neighbor data pointswith which to make a classifying prediction of a particular data pointwhere the prediction is the known output for the one or more nearestneighbor training points through calculating a distance between theparticular data point and the training point, sort the distances inincreasing order, taking the various items with shortest distances tothe particular data point, finding a majority class among the variousitems and then returning the majority class as the prediction for theclass of the particular data point. Such an approach may be particularlyuseful in correlating the LEAP data to symptoms that in turn may berelated to a particular disease or medical condition, particularly whencombined with access to symptom data that may be used to correlate theacquired data to a particular disease. In one form, the symptomdata—much like other forms of baseline data as discussed herein—may bemade to reside within memory (which in one form may be similar instructure or function to memory 173B as discussed herein) the backhaulserver 400, the cloud 500 or elsewhere that is accessible to system 1.As will be discussed in more detail below in conjunction with FIG. 6, amachine learning workflow may be used to take the acquired data to becleansed (or preprocessed), extracted and put into a feature vector inorder to have the data be in better form for subsequent analysis. By wayof example, such cleansing may include putting it into more unified,structured or standardized form, reducing its dimensionality, checkingfor missing values, arranging data by category, labeling data points,placing proper temporal sequencing, removing data outliers or the like.

Regression-based approaches tend to be used in situations where theclasses of acquired data are made up of a continuous (that is to say,measurable) set of numerical values for linear cases or countable valuesfor discrete cases. Regression-based models correlate a predicted (i.e.,output, or dependent) variable to a combination of coefficients (i.e.weights) multiplied by a set of input variables, and also use an errorterm to take random sampling noise into consideration as a way to comeup with a single estimate of the predicted variable based only ontraining data. Such models may be used to predict changes in thepatient's condition, compared to a classification model that may be usedto predict in a binary yes/no way what the impact will be of aparticular change in LEAP data. As will be discussed in more detaillater, ADL data (with its continuum of activity data) may be coupledwith a regression-based model in order to determine the likelihood thata particular patient is manifesting signs of cognitive decline or is atrisk of developing an infection or related adverse health carecondition. In one form, regression-based approaches may be used tocorrelate such changes through comparison of acquired data with known(or previous-acquired) baseline data. As previously mentioned, periodicoutburst that are associated with neuropsychiatric conditions may berelatively easily modeled using a regression-based machine learningapproach. Significantly, because the data being acquired by the wearableelectronic device 100 is complex, including coming from differentsensors 121 that are measuring different parameters, the inherentability of regression-based approaches to handle multiple variables mayform a useful way to correlate the various forms of acquired LEAP datainto a single predictive output. Regression-based approaches tend to beamong the fastest machine learning models.

SVM-based approaches are a supervised form of learning used to build amodel that represents data examples as points in space that are mappedin a manner to place the examples into clearly separated categories. Newexamples may then be mapped to opposing sides of a gap that is usedbetween the separated categories. In one form, SVM may be used inconjunction with the kinematic ones of the sensors 121 of the wearableelectronic device 100 to detect and classify various activities. Forexample, accelerometers, gyroscopes and other kinematic orspatial-sensing approaches, taken in conjunction with one another canmeasure ambulation, body movements and body orientations for a betterunderstanding of HAR; the use of SVM may be used for acquiring suchactivity or movement-related data, especially when the activity beingsensed is scarce or abnormal. These in turn may be correlated tostandardized activity codes such as those found in the Compendium ofPhysical Activities, a globally-used reference that provides a unifiedfive-digit coding scheme for the classification of specific physicalactivities by rate of energy expenditure with a corresponding metabolicequivalent (MET). As such, changes in postural orientation (such asthose associated with changes from sitting, laying down, standing,patient movement, walking, running, falling or the like) may be used toinfer levels of activity that may in turn be correlated to changes in anindividual's present condition relative to a previously-defined baselinecondition. Likewise, various movement anomalies, such as those associatewith gait or the like, may be sensed for correlation to a particularcondition such as how changes in gait could be tied to spatialnavigation difficulties that in turn may be tied to the breakdown ofcognitive mapping and the onset of dementia as all are identified astaking place within similar regions of the brain. This temporal framedata collected from the various sensors 121 may be processed and used toclassify activity into an ADL category, such as through comparison toknown ADLs that were previously acquired or stored in memory 173B (suchas in the form of a lookup table or the like). In a manner similar torandom forests and unlike the Bayesian classification or neural networksmethods, the SVM method may be used for training with small sets ofdata.

Clustering-based approaches involve grouping a set of objects thatexhibit high degrees of similarity through an iterative process thatinvolves trial and error. As such, clustering-based approaches arewell-suited for the detection of anomalies that may show up inimbalanced data sets where the various classes of data will have adifferent number of examples and where accuracy of the informationcontained in one or more so-called minority classes is important to thedecision-making process. Depending on the data being acquired, balancing(such as weight balancing discussed elsewhere herein or sampling-basedbalancing) of the data sets may be beneficial. In one form, a clusteringapproach is an unsupervised approximation to supervised classificationapproaches examples of which include expectation-maximization and kNN,particularly for their ability to ascertain a particular form ofcognitive impairment where the data is in the form of movement andrelated activity data where similarity or differences of such activityover time may be ascertained and then correlated to changes in patientbehavior and patterns. Thus, where the model is based on K-means orrelated clustering approaches, each data point in the data set may beconsidered a vector of valued attributes, which is the unsupervisedanalogue to supervised machine learning feature vectors except that thefeatures are not specifically labeled. In another form, clustering maybe understood to create parameter values and classification of theacquired data in order to provide identifying labels for observations asan initial step to converting unsupervised learning into supervisedlearning. For example, clustering-based models may be used in trying todetermine human activity recognition (HAR), ADL or instrumental ADL(IADL) by separating similar activities from dissimilar ones usingpattern vector partitioning.

In one form, K-means clustering can generate a minimum variance groupingof the LEAP data through minimization of the sum of squared Euclideandistances from centroids within the cluster that in one form mayinitially be chosen arbitrarily such that received data becomesdistributed among the chosen cluster domains based on minimum distances.Once the data has been distributed, the cluster centers may beiteratively updated to reflect the means of all the records in thecorresponding cluster domains until some measure of convergence isattained. In fact, K-means clustering could be used as the front-endpart of a hybrid unsupervised/supervised approach, particularly insituations where the amount of LEAP data collected is relatively small(such as from a single patient), making the model a K-meansclustering-based one. From the K-means front-end, the data can be re-runon a classification back-end. In this way, the differences between thereal-time data and the baseline data that otherwise may go undetected inanother approach that is not adept at analyzing such small, sparseamounts of data as correlate such differences to incipient changes inthe health condition of a patient become much more easily identified.K-means clustering identifies which data points belong to acorresponding one of the K clusters that, through a series ofiterations, creates groups (i.e., clusters) of these data points thathave similar variance and that minimize a given cost function, and doesso with relative ease of implementation especially in view of therelatively small amount of data being collected for a given patient.While K-means clustering is particularly useful when the substantialentirety of the acquired data set is used, some of the other clusteringapproaches, such as hierarchical clustering (for discovering embeddedstructures in the data) or density-based clustering (for discoveringhitherto unknown numbers of clusters, particularly when small groups ofnearby clusters may be segmented). As with other unsupervisedapproaches, K-means clustering may be good for exploratory analysis anddata sets where dimensionality reduction is important.

Thus, in one form, a hybrid supervised/unsupervised approach may startoff by having the model be trained on at least a portion of the LEAPdata as the person P associated with the wearable electronic device 100performs a one or more movements, such as hand gestures, transitionalmovements (such as those discussed in more detail in conjunction withFIGS. 11A and 11B) or the like that can be labeled. The acquired data(some of which may be taken from one or more of sensors 121) may then beprocessed to calculate signal spatio-temporal or other characteristicsthat may then be subjected to unlabeled clustering in order to assignthem to an appropriate cluster depending on the movement beingcategorized. HAR and related ADL or IADL categorization may then beperformed based at least in part on the clusters that were formed duringtraining. As such, this hybrid approach may be understood to make up aK-means clustering-based approach.

Dimensionality reduction-based approaches can produce a set of principal(that is to say, more relevant) variables that are more homogeneous andless voluminous than the raw data, especially by removing highlycorrelated features from the number of total amount of data acquiredfrom the wearable electronic device 100. This is helpful because thisdata involves both high frequency in the form of large numbers ofobservations per second and high-dimensionality in the form of myriadsensor 121 types that are collecting multiple forms of datasimultaneously such that each different sensor 121 forms an inputvariable that may correspond to one dimension of a multidimensionalspace. As such, dimensionality reduction may be used as factor analysisin order to group features together. As will be discussed in more detailbelow, such reduction may be a predecessor to, or be a part of, bothfeature selection (i.e., the approaches try to find a subset of theoriginal variables that contain accurate, relevant input data, examplesof which include neighborhood component analysis, regularization,sequential feature selection, stepwise regression or the like) andfeature extraction (also called feature transformation, where the datais placed into a feature space of fewer dimensions while maintainingaccuracy) as a way to spot rules, trends or related likelihoods. Thisreduction in the number of variables involved may help keepclassification—based models from overfitting. Together, such featureselection and feature extraction take the raw data that has beenacquired from the sensors 121 and form derived features for use insubsequent learning and generalization activities within the algorithmsused to create the corresponding model through stronger positive andnegative correlations. For example, one positive correlation may berelating the number of trips to the bathroom (which can be measureddirectly based on location data) to the increased likelihood of a UTI,while a negative correlation may be that sedentary activity (which alsocan be measured directly based on location data) is an indication thatthe patient being monitored is not exhibiting symptoms associated withagitation. One common form of dimensionality reduction algorithm is thePCA that through search optimization transforms feature variables into asmaller number of principal component variables; in this way, themodified vector representation of these features allows additionalinsight into how important they are to the output. Other forms ofdimensionality reduction may include kernel density estimators, Gaussianmixture modules, singular value decomposition, expectation maximizationor the like. In one form, these estimators may be used in conjunctionwith (or are supported by) various data sets, feature selection, modelselection and other machine learning modules to significantly improvethe ability of a machine learning algorithm to only have to useparameters that have probative value. Tuning of hyperparameters, whichinvolves identifying the most relevant parameters for a given machinelearning model, can be used in an iterative manner along with one ormore training algorithms as a way to help achieve dimensionalityreduction of the data. Other forms of transforming the featuresassociated with the acquired LEAP data to extract more relevant featurevectors may eigendecomposition, whitening transformations or thepreviously-mentioned LDA.

Referring next to FIG. 6, the various machine learning approaches may inone form follow an ordered sequence of operations performed on the LEAPdata acquired by the wearable electronic device 100. Moreover, thisordered sequence (which is referred to herein as a machine learningworkflow 1000) may include the following steps: (1) a raw dataacquisition (first) step 1100; (2) a raw data cleansing or otherwisepreprocessing (second) step 1200, such as through the use of signalprocessing via noise filters, normalization or the like as a way toseparate out various redundancies and related noise in order to processonly the data that will bring about a statistically significant increasein predictive or explanatory power; (3) a feature extraction (third)step 1300 of derived values which may include placing the data into theprevious-mentioned feature vector or related form, and which may involvesome form of data mining or related exploratory data analysis; (4) atraining (fourth) step 1400 for application of an iterative machinelearning algorithm to fit or create a machine learning model; and (5) amodel use (fifth) step 1500 with which to operate the trained machinelearning model on some or all of the acquired LEAP data (in particular,the real-time or other presently-acquired variants) in order to drawinferences from such acquired data. In one form, this ordered sequencemay be used to provide predictive analytics to assist in the diagnosis(such as through online analytical processing (OLAP)) or the like bydoctors, nurses and other caregivers C of the health condition of aperson P. In another form, the ordered sequence may be used to performits own autonomous diagnosis without human intervention. In yet anotherform, this ordered sequence may be used to perform an action plan sothat it can provide guidance on changes in medication dosages, changesin dietary or activity protocols, changes in occupational or physicaltherapy plans or the like. Moreover, because such diagnosis is based onthe acquired LEAP data that is specific to a particular individual, suchdiagnosis and the ensuing action plan could qualify as personalizedmedicine and related individualized-profile clinical decision-making.The first three steps 1100, 1200, 1300 form the core of data management,while the last two steps 1400, 1500 make up learning, inference orrelated analytics to acquire intelligence from the initial voluminousdata set. As such, it will be appreciated that the first three steps1100, 1200, 1300 may be performed independently—as well as part of—amachine learning-based analysis, and that both variants are within thescope of the present disclosure.

In one form, one or both of baseline data 1700 and presently-acquireddata 1600 (such as the activity portion of the LEAP data, for example)may be stored in memory 173B in an unstructured, flat file format suchthat during the cleansing or related preprocessing associated with thesecond step 1200 of the machine learning workflow 1000, improvements indata uniformity may be realized. In one form, grouping the acquired data(from either the wearable electronic device 100 or elsewhere, such aslookup table based on known prior data of a particular patient or groupof patients with similar health demographics) can be through anunsupervised clustering model; such an approach may be particularly goodat segmenting the data into several different groups. In one form, thisbaseline data 1700 may be annotated for use in training-based activity,behavior or related parametric information that can be compared toreal-time (i.e., presently-acquired) data in turn can be operated uponby one or more of the machine learning models discussed herein. Thebaseline training examples may include representative temporalsequences, including being further annotated or labeled in order to bescene-specific or situation-specific as a way to provide context for themodel, as well as for model training. In one form of baseline data 1700,a combination of one or more sensed or known parameters may be used toHELP define a behavioral profile so that the individual's dailyactivities are known. As shown by the three-dimensional representationof data in the figure, any or all forms of data may be expressed as avector V, array A or multidimensional array (that is to say, tensor T)in order to be in appropriate feature vector form for subsequent use ofthe independent LEAP data.

As part of the cleansing or preprocessing second step 1200, the acquireddata may be tagged or identified, including through the use ofspatio-temporal identifiers including location, time stamp, sensor class(for example, accelerometer, gyroscope, temperature, electrical,piezoelectric, piezoresistive or the like) or unique sensoridentification codes. Data acquisition libraries, such as thoseavailable from MATLAB (Mathworks, Inc., Natick Mass.), may be used toprovide sensor-based data acquisition support for such tagging andidentification; such support may include other forms of datapreprocessing, including class-labeling, noise filtering and relatedcleansing or scrubbing, as well as data balancing, all as a way totransform the data into a form that may be used by the subsequentfeature extraction, algorithm selection, training and eventualpredictive analytics model usage. In one example, the acquired data thathas been operated upon by some or all of these libraries may besubjected to receiver operating characteristic (ROC) analysis as a wayto quantify the performance of an activity classification algorithm. Inone form, such an analysis may be in the form of a curve to providevisual comparison between various classification models where the areaunder the ROC curve (AUC) provides a measure of a particular model'saccuracy. This model evaluation, which takes place once a model istested and evaluated, may also be based on other criteria such as meansquared error, accuracy, sensitivity, specificity or the like. In thisway, the activity classification algorithm can use known diagnosticperformance metrics such as ROC and area under the ROC curve (AUC)values, positive and negative predictive value, sensitivity, specificityor the like to allow a comparison against physician-based expertdiagnoses. Such an approach may be particularly beneficial when thereare imbalances in the classes of data being used as part of a particulardata set. In one form, a biquad or other high-pass filter may be used sothat the gravitational effects of the accelerometer may be removed toallow subsequent analysis (such as through one or more of the machinelearning models discussed herein) to focus on the inertial, movement ofthe patient rather than on the wearable electronic device 100. In thisway, bodily acceleration components are removed from the data, which inturn may be used to classify different movements (such as betweenrunning and walking) using nothing other than mean, root-mean-square andrelated statistical features of the signal. In one form, suchclassification may form the basis of a neural network. Likewise, filtersmay be applied to control data sampling rates or the like. In one form,features that capture the frequency content of the accelerometer datamay be extracted to distinguish between various ambulatory events (suchas walking versus running, climbing versus travel or level ground, orthe like) where ROC may be applied to each of the training, validationand testing data sets that will be discussed in more detail later. Inone form, statistical-based feature extraction may be used on the rawdata acquired by the accelerometer or accelerometers such that theresulting set of such features may be presented for use as input in asubsequently-created machine learning model. In one form, the featureextraction of sensed activity or movement data such as that acquired bygyroscopes, accelerometers or the like may be accomplished throughadders, multipliers, impulse filters, band-pass filters or relatedmathematical operation circuitry contained within the logic device 173or elsewhere. For example, peak analysis may be used to find importantfrequency content (such as through Fast Fourier Transform or the like),pulse and transition metrics to derive rise time, fall time and othersignal characteristics, as well as spectral measurements to determinebandwidth, frequency and power information.

While it is understood that different kinds of data may involvedifferent methods for the cleansing or preprocessing second step 1200,there are some methods that tend to be employed for almost all forms ofdata, including the LEAP data discussed herein. As part of this secondstep 1200 of the machine learning workflow 1000, the LEAP data that isbeing acquired by the sensors 121 may be filtered, amplified andconverted, either onboard the wearable electronic device 100 thewearable electronic device 100 or remotely (such as on the gateway 300or servers 400), in either event via local processor, memory andexecutable instructions. For example, the acquired LEAP data may gothrough a normalization process in situations where features (that is,the columns within a matrix or array of data) have different ranges.With normalization, the numeric values of the data are adjusted to acommon scale while substantially preserving differences in the ranges ofvalues in order to avoid gradient upsets (and a consequent failure toconverge) during subsequent optimization steps. In addition, theacquired raw data is typically transformed into vectors or relatedmeaningful numeric representation, as discussed elsewhere within thepresent disclosure. Thus, for every row of a particular type of data isconverted into suitable integer values as a way to populate an inputmatrix. Furthermore, the data may have missing values, in which eitherzero-value or interpolated mean value placeholders may be inserted intothe respective column of the matrix.

As previously mentioned, such cleansing or preprocessing second step1200 need not be a part of a machine learning-based approach, andinstead may be used for other forms of analysis where improvements indata uniformity and manageability are needed. Regardless of whether thevarious forms of data cleansing and other manipulation are used in amachine learning-based approach or not, the architecture of the wearableelectronic device 100 is such that it not only improves the operationand efficiency of the reception and transmission of various forms ofdata, but also of the data gathering itself in that by acting as asingle point for data gathering, the aggregation of the data gatheringand dissemination need not be dispersed over larger portions of anetwork. This in turn helps promote consistency of the data. Moreover,by providing a singular, unitary platform (such as through the housing110-based containment structure discussed previously in conjunction withFIGS. 2A through 2H, as well as the conformal configuration of FIG. 2I),the wearable electronic device 100 is able to provide for a relativelyunobtrusive wearer experience that is particularly beneficial forpatients with dementia or other cognitive frailties and who mightotherwise object to having to wear a device that has a more distributedarchitecture that employs multiple component securing locations.

This second step 1200 of the machine learning workflow 1000 is alsouseful in making subsequent analytic inferences from the LEAP data moretractable. For example, redundancy and size of an initial set of rawfeatures taken from the sensed data can make such data difficult tomanage, especially as it relates to providing a meaningful way toclassify a particular HAR, ADL, IADL or health condition. In particular,the acquired data is often diverse and complex, even for the same personP doing different activities at different times. The amount ofinformation associated with such baseline data 1700, as well assubsequently-acquired ADL or IADL data that is taken from sensors 121,is potentially voluminous, and often of a heterogeneous nature. Inaddition to ensuring that the data is uniform as a prerequisite forrendering it useful for its intended purpose of extracting machinelearning insights, another prerequisite may be to reduce itsdimensionality. Such dimensionality reduction may be seen as a portionof the second step 1200 of the machine learning workflow 1000 five-stepordered sequence. In one form, the data interpretation may be performedby one or more portions of machine code that are operated upon by one ormore processors 173A that are associated the wearable electronic device100, gateway 300, backhaul server 400 or cloud 500 such that output ofthe analysis is provided for use by a caregiver C. In one form, theresults of the analysis that are associated with such output may bestored in memory 173B, as well as provided in transient, real-time to adisplay, audio device, graphical user interface (GUI) or the like all ofwhich may form a part of the remote computing device 900.

In one form, the process of converting the raw LEAP data that is takenfrom sensors 121 into a form suitable for use in a machine learningalgorithm and subsequent model 1500 may form part of an activity knownas extraction, transformation and loading (ETL) that may make up part ofthe previously-discussed second and third steps 1200, 1300 of themachine learning workflow 1000. Within the present context, ETL may beused to decompose multi-sensor data into a suitable feature vectorwithin a given feature space that can then be correlated throughsubsequent fitting and evaluation of the fourth and fifth steps 1400,1500 of the machine learning workflow 1000 in order to produce one ormore model-based performance metric results for certain types ofpredictive analytic activities, such as those associated with amonitored individual's ADL, IADL or corresponding health condition wheredetermination or prediction of the health condition is in one formderivative of the ADL or IADL output and in another form is determineddirectly (that is to say, without the need to first determine ADL orIADL). By way of example, a feature space in two dimensions may berepresented through the two axes of a common x-y graph, while additionalrepresentations along a third axis (for example, the z-axis) may be madeto correspond to outputs, such as those of one of more hidden layers ina neural network in order to define a feature space in three (or more)dimensions in a manner analogous to a tensor. Within the presentdisclosure, the term “converting” and its variants are understood toinclude all steps necessary to achieve ETL functionality, includingcleansing of the data or reducing its dimensionality the latter of whichin the form of feature selection will be discussed in more detail later.

The models employed by system 1—which may include machine code 173E thatcan be written in or converted from one of several programming languagessuch as Python, Java, R or the like—as well as employing theircorresponding machine learning libraries or toolkits, such as MATLAB,NumPy, Weka, kernlab, SciPy, LIBSVM, SAS, SVMlight, Scikit-Learn,JKernalMachines, Shogun or others—engage in iterative approaches toupdate the decision-making process as a way to learn from the variousforms of data being acquired by the wearable electronic device 100 andits various sensors 121. For example, a machine learning library such asScikit-learn is used with the Python programming language to providevarious classification, regression and clustering algorithms includingSVM, random forests, gradient boosting and K-means. In addition, itoperates in conjunction with Python numerical and scientific librariesNumPy and SciPy. Moreover, APIs (such as TensorFlow, H₂O, Spark MLlib orthe like) may be used to help determine the best machine learning modelto use, while some of the libraries mentioned above may include unifiedAPIs to facilitate ease of use of a particular machine learning model.In one form, an open-source machine learning core library such as NumPyis used for performing fast linear algebra and related scientificcomputing within the Python programming language. NumPy provides supportoperations for multidimensional array and matrix (but not on scalarquantities) data structures, along with a large collection of high-levelmathematical functions to operate on these arrays. For example, thelinear equations that represent linear algebra are presented in the formof matrices and vectors that may be memory-mapped as data structures forcomputing complex matrix multiplication relatively easily. Because theLEAP data that is being acquired by the wearable electronic device 100and its various sensors 121 is multidimensional and takes place overtime for the same patient or individual, multidimensional datastructures known as Pandas (that is to say, PANel DAta Sets) may be usedfor the initial preprocessing of the LEAP data. As will be discussed inmore detail later, such LEAP data may be input into vectors such asPandas data structures (also referred to as dataframes) or NumPy arrayssuch that they can later be broken up into training data sets,validation data sets and test data sets for machine learning use.

Moreover, it is possible through feature extraction-basedparameter-reduction techniques such as gradient descent, backwardpropagation (also referred to herein as backpropagation) or the like toprune a network (such as a deep learning neural network) and improve themapping between LEAP data input and output to achieve minimized costfunctions associated with classifying the corresponding health conditionbeing predicted. Thus, at least in supervised machine learning models,feature extraction takes advantage of knowledge already known to helpprovide those predictive features most likely of use for a physician orother caregiver C in order to make a clinical diagnosis. Such reductiontechniques, as well as those associated with convolutional weightedkernels, filters, channels or the like, are additionally helpful intheir ability to reduce the processor 173A and memory 173B requirementsassociated with deep learning algorithms and models, thereby allowingthem to operate on mobile and embedded equipment in the form of wearableelectronic device 100. In one form, at least some of the acquired datamay be processed locally on the wearable electronic device 100, such asthrough an on-device embedded library, software module or the like Thisin turn may lead to inferring the successively more detailed ADL andIADL data. Depending on the available processing power and battery lifethat is on-board the wearable electronic device 100, increasinglycomprehensive portions of the assessment of the wearer's health statusmay be conducted locally, rather than at the system 1 level, and thatall degrees of computational operations conducted locally at thewearable electronic device 100 or remotely at the system 1 are deemed tobe within the scope of the present disclosure. For example, a comparisonof real-time LEAP data to a baseline data 1700 (such as that stored in alookup table or related data structure) in order to determine if thereis an excessive deviation may be performed locally on the wearableelectronic device 100. In another form, some or all of the acquired datamay be processed on the gateway 300 such that it promotes edgecomputing, fog computing or related functionality as previouslymentioned. In yet another form, some or all of the acquired data may beprocessed on the backhaul server 400 or cloud 500. In yet another form,some or all of the predictive analytics (such as that associated withthe one or more machine learning models discussed herein) may beperformed on any or all of the gateway 300, backhaul server 400 or cloud500. For example, in one form, the gateway 300 may perform some or allof the machine learning. Thus, in such form it may include functionalitybeyond that associated with sending data from the wearable electronicdevice 100 to the other parts of the system 1, the cloud 500 or thelike.

Within the machine learning context, various analogies and terms may beuseful in understanding how the LEAP data that is being acquired by thewearable electronic device 100 about the individual being monitored maybe correlated to information pertaining to the individual's location,mobility, heath condition or the like. For example, terms related to thedata being acquired, analyzed and reported include “instance”, “label”,“feature” and “feature vector”. An instance is an example or observationof the data being collected, and may be further defined with anattribute (or input attribute) that is a specific numerical value ofthat particular instance, while a label is the output, target or answerthat the machine learning algorithm is attempting to solve, the featureis a numerical value that corresponds to an input or input variable inthe form of the sensed parameters, whereas a feature vector is amultidimensional representation (that is to say, vector, array ortensor) of the various features that are used to represent the object,phenomenon or thing that is being measured by the sensors 121 or otherdata-gathering components of the wearable electronic device 100.Visually, the instance, label and feature can populate a data table (orspreadsheet) such as the previously-mentioned x-y graph or x-y-z graphwhere the instances may be listed as numerous rows within a single labelcolumn, whereas the features populate various labeled columns for eachrow. To think of it colloquially, the use of machine learning to solve aclassification, regression or other problem can be analogized topreparing a meal, where (a) the data being acquired by the wearableelectronic device 100 corresponds to the ingredients to be used, (b) themathematical code that is the algorithm is a sequence of actions thatmay be analogized to the tools, equipment, appliances or the like thatoperates on the ingredients, (c) the model is the recipe that is used inconjunction with the algorithmic tools to provide a framework forrepeatability and (d) the label is the desired output in the form of thefinished dish. Thus, the model may be understood as the recipe that isformed by using the correct number and quantity of ingredients from thedata that have been subjected to trial-and-error training through theuse of the tools that make up the algorithm. As such, the model is amathematical description of how to convert input data into a labeledoutput; a new model may be generated with the same algorithm withdifferent data, or a different model may be generated from the same datawith a different algorithm. Thus, within the context of machinelearning, the algorithms discussed herein are constructed to learn fromand make predictions in a data-driven manner based on the data beingacquired by the wearable electronic device 100, and from thesealgorithms, a model may be built for subsequent use in identifyingsalient indicators of the health of an individual that is beingmonitored by the wearable electronic device 100. In this way, the modelis the resulting output once a machine learning algorithm has beentrained by data.

In one form, the feature vectors (which may occupy a correspondingfeature space) are subjected to a scalar multiplication process in orderto construct a weighted predictor function. Moreover, featureconstruction may be achieved by adding features to those feature vectorsthat have been previously generated, where operators used to performsuch construction may include arithmetic operators (specifically,addition, subtraction, multiplication and division), equality conditions(specifically, equal or not equal) and array operators (specifically,maximums, minimums and averages) among others. In one form, theanalytics associated with these feature vectors may be performed inorder to ascertain classification-based results (for example, whetherthe sensed parameter or attribute is less than, equal to or greater thana threshold that may itself be based on a known relative baseline,absolute baseline or other measure of interest), or to perform aregression in order to determine whether the sensed parameter or itsattribute can be correlated to the likelihood of an event outcome.Within the present context, a feature vector could be a summary of oneor more of a patient's kinematic data (which may form indicia ofactivity) and related location data, physiological data, orenvironmental data such that the ensuing clinical observation ofsymptoms may lead to an enhanced diagnosis of a particular condition(such as UTI, Parkinson's disease symptoms or other neuropsychiatricconditions, for example).

In one form, some or all of the program structure that defines the lastthree steps 1300, 1400, 1500 (that is, feature extraction, algorithmictraining and use of the subsequent model to generate useful analyticaloutput or prediction) of the multistep machine learning workflow 1000may be embodied in machine code 173E. In this way, particular forms ofdata extraction may be performed through the manipulation of this datathrough the cooperation of the processor 173A and the machine code 173E,as can one or more of the machine learning algorithms discussed hereinfor use with the training and subsequent machine learning model-basedanalysis. As such, the use of mthe wearable electronic device 100, orwhether given that such condition is already present, whether it isbecoming worse.

Referring next to FIG. 7, an example of a machine learning model couldbe a neural network 2000 used to determine whether a person P from whichthe data is being collected is at risk of developing an adverse medicalcondition that could be meliorated through an early intervention actionplan. By way of example, the analysis using the neural network 2000 maybe in the form of a classifier or regression-based approach either ofwhich that trains on one or more components of the LEAP data in order todetermine if a person P to whom the wearable electronic device 100 isattached is in danger of developing a UTI; such predictive input datamay in one form be used in lieu of—or in conjunction with—traditionaldescriptive indicia of UTIs, including baseline data 1700 such as thatgathered from the medical records of person P or from a representativepopulation database (either of which may also be cleansed, formatted orotherwise extracted in order to make up one of the previously-discussedfeature vectors) as a way to produce a classified output. In oneparticular form, the health change being analyzed by the neural network200 pertains to UTIs where historical indicia, such as uropathogencounts, CFU/ml, leukocyte esterase, epithelial cells, dipstick analysisor the like, while useful in traditional clinical diagnoses where aphysician already has some advance warning of a UTI, are generally notavailable until such time as after the physician has either (a) seen apatient who has already become symptomatic or (b) conducted variousdiagnostic tests after having seen a patient and determined that a UTImay be present. Instead of such retroactive mitigation of analready-present likelihood of a UTI, the data collected by the wearableelectronic device 100 and processed either on the wearable electronicdevice 100 or other parts of the system 1 or cloud 500 may be used in aproactive way in order to prevent the UTI from happening in the firstplace.

Additional details associated with the neural network 2000 are presentedin more detail as follows, particularly for diagnosing UTIs and otherimpending health condition changes. As shown, the neural network 2000includes an input layer 2100, at least one hidden layer 2200 and anoutput layer 2300. In a deep learning form, multiple hidden layers 2200may be used in order to perform increasingly complex analyses; such deeplearning is a subset of neural networks where the number of hiddenlayers 2200 is greater than one. With deep learning (often with the helpof advanced processors such as the previously-mentioned GPUs), thesestacked hidden layers 2200 can help the system 1 to learn by creatinghierarchical representations of certain activities, wheresubsequently-created layers form an abstraction of previously-createdlayers as a way to formulate an understanding of complex correlationsbetween the acquired data and a desired output such as a particularhealth condition. In one form, the hierarchical representation may beused to depict relationships between different primitive actions andmore complex actions, where a binary decision process is repeated untilthe system 1 correlates the data being sampled to movements that may becorrelated to HAR or ADL. Moreover, the architecture associated with theneural network is such that it may be developed using a hierarchicalapproach as a way to readily accommodate new forms of, or relativeweighting to, the input data, including those forms of data thatrepresent varying degrees of activity granularity or detail, where inone form such granularity may be associated with how frequentlytemporal-based data (such as activity data) is being acquired. By way ofexample, in situations where an analysis of movement is being performed(such as to develop insight into a patient's movement signature),accelerometer data may be fed into a decision tree to allow binaryclassifications over multiple decision layers. As such, a first leveldecision may be whether there is movement or not, whereas a second leveldecision may be to determine the type of movement (in the event that thefirst level detected movement such as distance traveled, number of stepstaken, number of stairs climbed or descended, or the like) or theorientation or position of the wearer (in the event that the first leveldetected no movement). Likewise, subsequent levels may be used todetermine transitions from one movement or position to the next, as wellas what such movements mean within a larger context such as socialsetting, scene-specific position, and so on. From there, the variouscomplex movement or lack thereof may be correlated to various classifiedsignals. In one form, implementation of a supervised form of a deeplearning model may generally follow the last four steps 1200, 1300, 1400and 1500 of the five-step machine learning workflow 1000 that waspreviously discussed in conjunction with FIG. 6, with slight variationsto the machine learning workflow 1000 in order to account for deeplearning's specific functionality. For example, a deep learning neuralnetwork may (a) initialize parameters and define hyperparameters, (b)iterate over the network using forward propagation and backwardpropagation for parameter updates, (c) use trained parameters to predictlabels and (d) test the predictions on examples.

In one form, the data of the input layer 2100—once suitablypreprocessed, extracted and algorithmically trained—can be used todetermine through the model and its back-and-forth among the one or moremultiple hidden layers 2200 in order to arrive at the output layer 2300that represents a desired target or output. For example, the outputlayer 2300 may correspond to ADL or IADL the latter of which may bethrough a composite of more primitive ADL data that itself is either thetarget of a prior operation or its own form of input features such asthat used as the input layer 2100. Within the present context, thecomposite nature of the IADL versus the ADL may be aggregative in thatit involves inferring the former from various disparate types of thelatter, while in another it may be temporal in that it involvesinferring the former from the sequential correlation of a series of thelatter. Likewise, it could be a combination of both aggregative andtemporal components, and that all such composite variants are deemed tobe within the scope of the present disclosure. Moreover, the ADL datamay in turn be based on location data as well as on more coarse positionor activity (including HAR) data, environmental data and physiologicaldata all as discussed elsewhere within this disclosure. In this way, itwill be appreciated that the ADL may be a composite of various forms ofHAR data in a manner similar to how the IADL is a composite of the ADL,and that the IADL could be a composite of one or both of the HAR andADL. In one form, the ADLs include postural or ambulatory activities ina manner similar to—or based upon—HAR information, while IADLs includethose activities that reflect both physical and cognitive components. Assuch, a deep learning form of the neural network 2000 can combine one ormore LEAP events in order to convert them into complex (i.e. multimodal)events that correspond to ADL or IADL. If it is assumed that a certainpercentage of the data being sensed is of questionable validity, abagging or related probabilistic selection approach may be employed toensure more relevant data, while dimensionality reduction may be used toremove redundant (that is to say, non-orthogonal) data that otherwisecould degrade the performance of the model, particularly when used toperform activity classification. As will be discussed later, reductionof such redundancy is particularly beneficial when analyzing theacquired data in order to form HAR, ADL or IADL analyses while reducingthe risk of the model over-generalizing. From such dimensionalityreduction or related removal of data redundancy, an ensemble or otheraggregating classification algorithm or model may be enabled. In otherwords, the inclusion of a new feature from the list of various ADL orIADL activities provides additional input data with which to conducteven more detailed machine learning analysis.

Such a model, as well as its corresponding algorithmic tools, mayinclude configuring processor 173A to execute programmed softwareinstructions by using a predefined set of machine code 173E such thatthe neural network which may receive data from the various sensors 121in the form of various nodes 2100A, 2100B, 2100C . . . 2100N of theinput layer 2100, where each of these input nodes 2100A, 2100B, 2100C .. . 2100N can store its corresponding input data value within aparticular location of memory 173B. In one form, the data correspondingto the various nodes 2100A, 2100B, 2100C . . . 2100N of the input layer2100 could be that of the LEAP data taken from the various sensors 121,the hybrid wireless communication module 175, inter-patient orintra-patient baseline data or other sources that would contribute tothe analysis performed by the neural network 2000 for the determinationof HAR, ADL, IADL and related changes in a patient's health condition.In addition, the one or more hidden nodes 2200A, 2200B, 2200C . . .2200N of the hidden layer 2200 may be connected to each input node2100A, 2100B, 2100C . . . 2100N such that the computational instructions(which are based on, or in the form of, activation functions such asstep/threshold, binary sigmoid, piecewise linear, Gaussian thatcorrespond to hyperbolic tangents, rectified linear units (ReLUs) or thelike that are used to introduce nonlinearity into the feature space of aneural network) that are implemented in machine code 173E that form apart of the processor 173A, may be used to calculate output nodes 2300A,2300B, 23300C . . . 2300N of the respective output layer 2300.Furthermore, each of the one or more output nodes 2300A, 2300B, 2300C .. . 2300N can store its corresponding output data value within aparticular location of memory 173B in such a way that an output signalprovides indicia of one or more classifications performed by the model.By way of example, each block or portion within these figures mayrepresent a module, segment or portion of machine code such as thatwhich forms one or more executable instructions for implementing aparticular specified logical function or functions. As such, each block,module, segment or portion may be implemented by the particulararrangement of the components and systems discussed herein, especiallyto perform the specified functions or acts needed for operation of thewearable electronic device 100 and system 1 and ancillary structure.

The neural network 2000 is meant to mimic the neurons in the human brainthrough the various interconnecting nodes 2100A through 2100N, 2200Athrough 2200N and 2300A through 2300N of the three primary layers 2100,2200 and 2300 such that a model trained with these nodes may determine aresponse in the output layer for data received into the input layerthrough adjustable weights W made within the hidden layer or layers2200. The interconnectivity between the nodes of the various layersensures that the nodes from one layer influence nodes from the otherlayers to allow the neural network 2000 to observe all components of theacquired LEAP data, as well as how the disparate pieces of such data mayor may not relate to one another in a manner that roughly analogizes thehuman brain. As with the Bayesian-based approaches (and unlike thedecision trees and logistic regression mentioned previously), neuralnetwork methods typically work with large data samples. In someinstances (i.e., deep learning) multiple hidden layers 2200 may be usedin order to perform increasingly complex analyses through regularupdates of the neural network model by using some labeled data and evenmore unlabeled data. The parallel or connectionist-based computation ofthe neural network 2000 differs from the sequential processing of mostcomputer architectures in that it forms a specific cycle in order tomake logical inferences, which in turn facilitates heuristic discoveryof both non-linear and multiplicative relationships between myriad inputand output variables. As such, a neural network may process multiplevariables (often in the form of hundreds or thousands of input nodes) tomodel complex relationships such as identifying the onset andprogression of infections, cognitive impairment or other diseases orrelated health conditions. As with neurons in the human brain, each ofthe nodes of the neural network 2000 either acts as an input (nodes2100A through 2100N), output (nodes 2300A through 2300N) or performs asingle activation function (nodes 2200A through 2200N) such as thosediscussed previously, rather than attempting to model an entirerelationship in the a priori logic of most computer models. For example,the input nodes 2100A through 2100N of input layer 2100 take in valuesof attributes relevant to the receipt of various forms of the LEAP datato be subsequently manipulated by weights W for use in the hidden nodes2200A through 2200N of the hidden layer 2200.

One way to achieve the training step 1400 of FIG. 6 for the neuralnetwork 2000 of FIG. 7 is to use a multi-pronged algorithmic approach.In such an approach, for each particular node n, a weighted hypothesis H(also referred to as a hypothesis function) that represents a certainoutput (i.e., target or response) created through the operation of afeedforward algorithm 1410 in conjunction with an activation algorithm1420 is imposed upon the input data such that when different from adesired target value TV based on a cost function algorithm 1430 is thensubjected to a backpropagation algorithm 1440 in order to correct (thatis to say, educate) the initial hypothesis H through reduction in a costfunction CF (also referred to as a loss function or an objectivefunction the latter of which occurs when the data is not linearlyseparable as a way to reduce the influence of data points on the wrongside of a hyperplane or related linear decision boundary) through anadapting algorithm 1450 that updates the weights W. Examples ofalgorithms used as part of the training step 1400 may include conjugategradient, gradient descent, Levenberg-Marquardt, Newton's Method andQuasi-Newton. In one form, these algorithms may be used to fit tocertain weights W such that the training parameters are used to developthe hypothesis H.

In particular, the input of the LEAP data from one or more of thesensors 121 and the first and second wireless communication sub-modules175A, 175B and that are associated with the nodes 2100A through 2100Nare fed through the feedforward algorithm 1410 to the hidden layer 2200while being manipulated by the weights W and the activation algorithm1420. The new values generated with the hidden layer 2200 are thenforwarded (also through the feedforward algorithm 1410) to the outputlayer 2300, while being manipulated again by the weights W andactivation algorithm 1420. The feedforward algorithm 1410 is of thegeneral form

Hn _(l)=σΣ_(l−1)(W _(l) i _(l−1))

where σ is an activation function represented by the activationalgorithm 1420, Hn_(l) is the hypothesis H value at a given node n on agiven layer l, while W is the weight value being applied to layer l, andi is the value on the immediately preceding layer. In this way, n and lact as respective shorthand for the previously-discussed nodes 2100A . .. 2100N, 2200A . . . 2200N or 2300A . . . 2300N and layers 2100, 2200and 2300. All of the input values associated with the LEAP data beinganalyzed are fed to each of the respective nodes 2100A . . . 2100N ofthe first layer 2100. In this way, each node 2200A . . . 2200N of thefirst of the hidden layers 2200 receives the sum of all the nodes 2100A. . . 2100N from the input layer 2100 multiplied by the weights W, afterwhich the summed value is then subjected to the activation algorithm1420 to generate a randomized output (at least in situations where theinitial choice of weights W is random).

In one form, the activation algorithm 1420 may be described by theactivation function σ (also referred to as the logistic function orstandard sigmoid function) that is of the general form

$\begin{matrix}{{\sigma(x)} = \frac{1}{1 + e^{- x}}} & \;\end{matrix}$

such that the value of the output σ(x) is always bounded between zeroand one so that regardless of how high or low the value of the input xas a way to provide a nonlinear representation of a weight or confidenceassociated with node n. It will be appreciated that other activationfunctions besides the sigmoid variant of the activation function σ maybe used.

After the application of the feedforward and activation algorithms 1410,1420, the target of the hypothesis H is compared to the desired targetvalue TV in order to calculate the cost function CF through the use ofthe cost function algorithm 1430 that is of the general form

C=½(Hn _(l)TV)²

After this, the backpropagation algorithm 1440 is run in order to exposethe error from the cost function CF to the derivative of the activationfunction σ to educate the hypothesis H based on the margin of the error.In this way, the error generated within each layer may be thought of asa recursive accumulation of change up to that time that has contributedto the error as seen by each node n. The back propagation algorithm 1440is of the general form

δ_(l)=(ΔE _(n) _(l) *σ′(Hn _(l)))

where past values of the weights W are subjected to chain rule partialdifferentiation in order to fit the following layer of nodes n, as shownby the sequence below

$\frac{\partial E}{\partial n} = {\delta_{L} = \left\lbrack {T{V\left( {W_{l + 1}*\delta_{l + 1}*{{Hn}_{l}\left( {1 - {Hn_{l}}} \right)}} \right\rbrack}} \right.}$

The gradient of these randomly initialized weights W and biases B of theneural network 2000 may be obtained by the previously-discussed backwardpropagation in conjunction with a library such as Numpy. Regardless ofhow it is conducted, this allows correlation to an individual one of theweights W by multiplying it by the weight's activated input node valuethat is of the general form

$\frac{\partial E}{\partial W} = {\frac{\partial E}{\partial n}*{Hn}_{l - 1}}$

The changes are then used in the adapting algorithm 1450 to adapt thevalues of the weights W where η represents the learning rate. Theadapting algorithm 1450 is of the general form

$W = {W - \left( {\eta*\frac{\partial E}{\partial w}} \right)}$

By repeating these algorithms over and over again, the future inputs aremanipulated in a manner such that the accuracy of the hypothesis H willimprove. Thus, the training step 1400 determines errors in hypothesis Hthrough the computed cost function CF, after which the weights W may beadjusted; such an approach is particularly useful in situationsinvolving imbalances in the training data set 1610. In one form,iterations may be used to test the various algorithms through the use ofa confusion matrix that compares classifications made by the trainedmodel with known quantities from labeled examples. More particularly forthe present disclosure, the training step 1400 allows the final neuralnetwork 2000 to be optimized for a particular sensed parameter, such as(i) location information (such as amount or time of day spent in aparticular room), (ii) local environmental conditions such astemperature, smells, time of day or the like, (iii) amount or frequencyof patient ambulatory activity or (iv) various physiological conditionssuch as body temperature, heartrate or the like. In this way, eachparameter being measured by the wearable electronic device 100 may haveits corresponding neural network 2000 of FIG. 7 that has been trainedusing the five-step machine learning workflow 1000 of FIG. 6 with thealgorithms 1410 through 1450 of FIG. 7.

Moreover, as mentioned elsewhere in the present disclosure, the trainingstep 1400 (along with its associated algorithms 1410, 1420, 1430, 1440and 1450) and use step 1500 of the five-step machine learning workflow1000 (as well as the ensuing model) may be performed on anysuitably-configured processor 173A and memory 173B combination that issituated on the wearable electronic device 100, server 400, cloud 500 orother equipment that is associated with system 1, subject to processingand storage capabilities. As such, any of the previously-discussed ASIC,CPU, FPGA, GRU or other comparable integrated circuit (IC) that is usedwith or forms part of processor 173A can be used to execute a machinelearning algorithm that has been trained on LEAP data acquired by thewearable electronic device 100 and stored in memory 173B, possibly inconjunction with baseline data 1700 from sensory or other sources foruse in a resulting model. It is also possible to perform differentportions of the five-step machine learning workflow 1000 with differentpieces of equipment associated with system 1. For example, in one form,at least the training may be performed as part of a cloud-basedapplication, while at least the data acquisition and some preprocessingmay be performed by the wearable electronic device 100. Other portionsof the workflow 1000, such as extraction and analysis may be performedon some of all of the components discussed herein. In other forms, someor all of the training and analysis may be performed on the wearableelectronic device 100, server 400, cloud 500 or other equipment that isassociated with system 1.

The neural network 2000 depicted in FIG. 7 is but one type of machinelearning model. In one form, the neural network 2000 may be a recurrentneural network (RNN) such as the previously-discussed LS™ in order tomimic the highly recursive networks in the human brain through loopsthat allow information to persist, as well as to take advantage of thetime series nature of the acquired LEAP data and the associated need topredict arbitrary future location, activities or behaviors. Such anapproach may in turn perform forecasting or prediction of time seriesevents, particularly those that take place over relatively longsequences. The RNN version of the neural network 2000 is particularlyadept at modeling sequential data such as that taken from sensors 121,particularly those which are capable of spatio-temporal dataacquisition. In one form, an RNN may be represented as an expandedfeedforward network through the inclusion of a recurrent loop, one ormore state variables or the like. In such case, a RNN in a given statemay be deemed the equivalent of the output of one or more of a neuralnetwork's hidden layers 2200. RNNs are helpful in overcoming situationswhere a conventional feedforward neural network would otherwise notoperate because of the latter's inability to accept sequential data,work with inputs of different sizes or utilize memory to storeinformation about previous states.

Other forms of the neural network 2000, such as a CNN and related deeplearning approaches, are well-suited for computer vision and relatedapproaches where kernel-based feature extraction operations forspatial-based information are required. Thus, whereas thepreviously-discussed RNN extends a neural network across time, CNNextends a neural network across space. More particularly, while twodimensional CNNs may be employed for image recognition problems,one-dimensional variants work well for identifying simple patterns fromshorter segments of acquired data, such as time sequence data acquiredfrom accelerometers or gyroscopes that make up a portion of the activitysensors 121B. In one form, such a machine learning model used to predictan individual's health condition based on the acquired LEAP data cantake in the raw input from sensors 121 and pass the data to one or moreof these intermediate hidden layer 22 filters to process throughweighted kernels that are trained to detect specific features with ahigh degree of correlation to a known quantity. For an example asapplied to the present disclosure, these weighted kernels may be in theform of the symptoms or symptom intensity of a particular healthcondition (such as a UTI, cancer, cognitive impairment or the like), orto the types of movement, activity or behavior of a patient that has ahigh degree of correlation to the particular health condition. Thus,even though CNNs—with their convolutional layers and pooling layers—arecommonly used to analyze handwriting, facial recognition and otherimage-related data, they may also be used in HAR or ADL for therelatively coarse accelerometer and gyroscope data that is beingprovided by the wearable electronic device 100. Unlike other machinelearning models, CNNs are capable of automatically learning featuresfrom time-based sequence data, in order to provide direct output for usein multi-step forecasting, such as what will happen to a patient over afuture period of time, such as the next twenty four hours in a givenday, the next seven days in a given week, the next month, or the like.In addition, a CNN may be trained through the use of relatively simplemodular approaches such as the previously-mentioned Keras API,especially in situations where the size of the training data sets isrelatively small, as well as situations where the analysis is beingconducted on the cloud 500, such as through AWS, Microsoft Azure, IBMCloud or the like.

As mentioned in conjunction with FIG. 6, baseline data 1700 may bepredetermined, user definable or acquired from known inter-patient orintra-patient standards or norms all of which may be stored in one ormore databases within memory 173B. As such, baseline data 1700 thatcorresponds to a health condition may be in the form of (a) personalizeddata that is specific to the individual associated with the wearableelectronic device 100, (b) specific to a particular group that mayexhibit one or more demographic similarities, or (c) universal such thatit covers relatively broad swaths of the population at large. Oneexample of a such baseline data 1700 may be acquired from the NationalInstitute of Health (NIH) Unified Medical Language System. Suchadditional information may include conventional UTI indicia such as thepreviously-mentioned uropathogen counts, CFU/ml, dysuria, leukocyteesterase, epithelial cells and other parameters such as those detectableusing a dipstick analysis (such as specific gravity, pH, leukocytes,nitrites, proteins, glucose, ketones, urobilinogen, bilirubin anderythrocytes). Moreover, the use of the data acquired by the wearableelectronic device 100 may be performed as part of a larger causal-basedmedical inquiry such as those using network science that may furtherinclude the use of a phenotypic disease network. In one form, either orboth of the intra-patient and inter-patient baseline data 1700 may beembodied in data structures stored in various data managementconfigurations, such as in memory 173B that is either local to thewearable electronic device 100, the system 1, the cloud 500 orelsewhere. For example, sensor-acquired data may be stored in a datamanagement center such as that contained within Microsoft Azure or arelated cloud-based computing service. In one form, the interpatientbaseline data 1700 may include any physical, cognitive, neuropsychiatricor related clinical condition.

In one exemplary form, the various flow diagrams of FIGS. 6, 11A, 11Band 14A through 14C, in addition to the neural network of FIG. 7, formprogram structures while the arrays (including the previously-discussedmultidimensional arrays), linked lists, trees or the like of FIGS. 3Athrough 3K, 8, 12 and 13 form data structures both of which constitutespecific structural features or elements that are recited in one or moreof the claims and that help to illustrate the architecture and operationof the various forms of the wearable electronic device 100 and system 1.Thus, by describing the various computer software elements inconjunction with the various functional activities that are depicted inthese flow diagrams, neural networks or the like, the machine code 173Ecooperates with one or both of the processor 173A and memory 173B toperform a set of particular manipulations of the acquired LEAP data toconstrain the operation of one or both of the wearable electronic device100 and system 1 in in a particular way for the purposes of identifyingpatient, activity, location or health condition. In one form, these maybe used for situations where one or more machine learning algorithms andmodels are being used to convert the data that has been acquired throughthe operation of the wearable electronic device 100 intoclinically-relevant predictions. In a similar manner, the cooperationbetween these structural features is such that they perform a set ofoperations in response to receiving a corresponding instruction selectedfrom a predefined set or portion of such machine code 173E fornon-machine learning operations as well. For example, in one form wherethe acquired data is being used to determine if a person P associatedwith the wearable electronic device 100 is at risk of developing a UTIor other identifiable medical condition, a classification-based neuralnetwork machine learning model may be carried out on one or both of thesystem 1 and cloud 500, as well as (depending on its computationalcapability) the wearable electronic device 100.

Regardless of the form of the machine learning model, some trainingunder the fourth step 1400 of the machine learning workflow 1000 may beused in order to allow the model to adapt to new, changing data. Thus inone form, where the data is used to build a machine learning model forpredicting health conditions about a patient or group of patients comesfrom the presently-acquired data 1600 from the LEAP data, it is brokenup into three sets known as the training data set 1610, the validationdata set 1620 and the testing data set 1630. With supervised learning,the training data set 1610 provides a set of examples used to fit theparameters through weighting. In one form, the acquired data that isinitially segmented into the training data set 1610 may be between about70% and about 75% of the total data, while the remaining 25% to 30% isreserved. In one form, the process of segmenting the data may beperformed in a random manner, while in another by known algorithms, suchas regularization algorithms and others found in a Scikit-Learn functionlibrary for the previously-mentioned Pandas dataframes or NumPy arrays.All such data set splitting helps ensure that the model is notoverfitting, or that the predictor variables associated with the inputdata avoid a covariate shift associated with an improper choice oftraining and testing data sets 1610, 1630. In a supervised learningmodel, the already-known paired inputs and outputs form the trainingdata set 1610, and in one particular form may be labeled in a vector,matrix, array or other data structure for ease of identification andpreviously-accepted veracity such as that associated with ground truthannotated human data. For example, annotated HAR data for training mayinclude those associated with publicly available or proprietary datasets. More particularly, at least some of the inputs of the sensed datamay be arranged into features within the data table such that multipleinputs form an input vector, one exemplary form of whichaccelerometer-based or gyroscope-based activity data. As will beapparent from the totality of the present disclosure, the attributes ofthe acquired data correspond to—upon suitable cleansing andextraction—features that may be stored (such as in memory 173B) inquantifiable feature vector form. In addition, the labeled answers arein the form of targets (for example, a scalar) that the machine learningmodel is trying to predict. Similarly, some of the input data may beincluded in a scalar quantity, such as that associated withtemperatures, pressure or other forms of environmental data; this toomay be included in the data table. In one form, the size or number ofentries of such data within a table corresponds to the dimensionality ofthe previously-discussed data structures that are part of memory 173B.

In one form, counting how many instances in the training data set 1610fall outside the training data set 1610 space helps to determine whichvalues constitute an error of false positives and false negatives thatcan be minimized by balancing specificity and generality of thehypothesis H. The preliminary model that results from the use of one ormore training algorithms on the training data set produces a resultwhich is then compared with the target. Various modes of testing thetraining data set 1610 may be used, including leave-one-out or differenttypes of cross-validation such as n-fold cross-validation. The resultsare in turn used to adjust the preliminary model's parameters to promotebetter fitting (that is, to avoid overfitting).

The validation data set 1620 is then introduced to provide an unbiasedevaluation of how well the fitted preliminary model from the trainingdata set predicts an outcome. In one form, the validation data set 1620is taken from the data that was reserved from the initially acquireddata. In this way, the algorithm is being trained without learning fromthe validation data set 1620. In one form, this outcome of observationsmade in the validation data set 1620 may be used to decide when thepreliminary model can stop training, such as to avoid the overfittingmentioned previously. In one form, when the machine learning model is inthe form of the neural network 2000 of FIG. 7, the validation data set1620 may also be used to tune the model's hyperparameters (that is tosay, the number of hidden units) in view of the fact that the correctanswer is already known at this time. In addition to evaluating how wellan algorithm fits on the training data set 1610, the validation data set1620 may be used to tune the various hidden nodes of the hidden layer2200 of the neural network 2000 in order to enhance the ensuing model'spredictive ability. In one form, the tuning of the nodes 2200A through2200N of the hidden layer 2200 is through the use of the presethyperparameters that (along with input data) define the structure of thenetwork being built to allow the network to be tailored to a specificimminent adverse health condition. Stated another way, hyperparameteroptimization involves finding the hyperparameters of a particularmachine learning algorithm that produce optimum performance whenmeasured on a validation data set 1620. Examples of such hyperparametersinclude the previously-discussed hidden node activation functions andweights W, as well as others including the algorithmic weight optimizers(examples of which include stochastic gradient descent, batch gradientdescent or mini batch gradient descent), number of layers 2100, 2200,2300 (that is to say, depth), number of hidden nodes 2200A through 2200N(that is to say, width) within each layer, learning rates (that is tosay, size of the parameter updating steps) and mini-batch size (that isto say, the number of training examples that are used to update theparameters). In another form, default values for one or more of thehyperparameters may be used. Moreover, certain approaches that performevaluations based on previous trials, such as Bayesian-based ones, maybe particularly good for determining hyperparameters in atimewise-efficient manner to help them reason about the best set ofhyperparameters to evaluate.

Finally, the testing data set 1630 is used to provide an unbiasedevaluation of the performance of the final version of the algorithm thatwas fitted to the original training data set 1610. The testing data set1630 is independent of—but statistically similar to—the training dataset 1610, thereby minimizing any adverse effects from discrepancies inthe data, as well as providing a challenge opportunity to determinewhether the preliminary model satisfactorily performs on previouslyunseen portions of the LEAP data. In particular, because the testingdata set 1630 already contains known values for the target, it is easyto determine whether the predictions made by the preliminary model arecorrect or not. Once the algorithms have been fully trained and analyzedwith all three data sets 1610, 1620 and 1630, they can be used as aclassifier or other machine learning model.

Within the context of a classifier-based machine learning model, atraining operation such as the previously-discussed training step 1400of FIGS. 6 and 7 is used to learn classifier parameters from theacquired data through the conversion of activity or other forms ofbaseline data 1700 into feature vectors through the previously-discussedalgorithmic cleansing and subsequent extraction activities. Testing ofthe extracted data may take place as part of an algorithmic fittingactivity. Subsequent evaluation of the acquired LEAP data may takeplace, including comparing between the extracted feature vectors of thebaseline data 1700 and presently-acquired LEAP data. Stated another way,in one form, at least a portion of the training function performed bythe machine learning algorithms discussed herein is made up of one ormore of representation, evaluation and optimization functions in orderto (i) convert the raw data into more useful form, (ii) define throughthe previously-discussed cost function CF what learning therepresentation function will undertake and (iii) optimize therepresentation function as a way to improve the evaluation metric forsubsequent model-based analysis of the acquired LEAP data.

Depending on their architecture, types of data accepted, number oflayers used, network topology, types of activation functions used andthe way they are trained, the neural network 2000 may be generallylumped into either (i) a feedforward neural network or (ii) a forwardpropagation or backward propagation recurrent network, although othercategorization is also possible. Within these broad categories, numerousparticular architectures may be used, including some of those aspreviously discussed such as perceptrons and CNNs in the first, andlong/short term memory networks, Hopfield networks, Boltzmann Machinenetworks and deep belief networks in the second. Neural network 2000 isparticularly adept at retaining historical input data, due at least inpart to its interconnected nodal arrangement such that the temporaloutput of previous nodes (for forward propagation variants) or previousor subsequent nodes (for recurrent variants) are used at any particularnode. The hierarchical cluster of simulated neurons in a CNN or otherform of neural network 2000 may be used in such a way that each of theneurons may detect low level characteristics of an input stimulus, afterwhich they can communicate with one another within the hierarchy inorder to develop a high level detection of one or more objects that areassociated with the acquired data through a “big-picture” type ofaggregation. In this way within a CNN, there exists locally connectedpatches of nodes between layers rather than as being fully connected ina traditional multilayer neural network. Weights W are used to spatiallyextend the network. Thus, at least certain variants of the neuralnetwork 2000 may take advantage of this hierarchical structure in orderto learn increasingly complex feature representations from the acquiredraw data. In other words, the raw data taken from the input layer 2100of a deep learning version of the neural network 2000 may be transformedinto more complex feature representations by successively combiningoutputs from a preceding layer. By way of example, early detection of apatient at risk for developing a UTI may be made by analyzing one ormore sensed features, such location or activity data that may indicatethe number of times or how recently a patient had a drink, how often orhow much time the patient spent in the bathroom (possibly in conjunctionwith the drink-specific data) or other time-related activities. In oneform, such early detection may then be followed by subsequentphysician-directed testing and diagnoses, such as taking a urineculture.

As previously mentioned, the physician may be one of the caregivers Cwho receives through his or her remote computing devices 900 the outputthrough the third wireless sub-module 175C of a machine learning modelwhere such output may be in the form of CDS information or the like. Assuch, a physician may use the results of a machine learning model suchas that taught with data acquired by the wearable electronic device 100to help predict the likelihood of an imminent onset of a disease such asa UTI, dementia or the like, as well as situations where comorbiditiesmay exist where one condition may be linked to another. For example, apatient who may be manifesting early signs of Alzheimer's disease may beunable to recognize a change in his or her health status, as well unableto convey such changes to a physician, nurse, family member or othercaregiver C; use of data acquired by the wearable electronic device 100in conjunction with a machine learning model such as that discussedherein could be used to anticipate whether such patient is at risk ofdeveloping a UTI in such situations even absent direct communicationfrom the patient. Relatedly, using such a machine learning model withthe acquired data in order to divine changes in ADL or IADL patterns mayin turn be used to provide early insight into whether the patient issymptomatic for Alzheimer's disease as well as other forms of dementia.As mentioned previously, training for these models may include comparingthe extracted data (in the form of features, feature vectors or thelike) to corresponding baseline data 1700 such as predicted data from aninter-patient or intra-patient set of control data model, after which anerror value may be generated that in turn may be used (such as throughsubsequent weight W adjustments or the like) to update a trainingalgorithm. In another form, the output may in turn form the basis foradditional analysis such that a machine learning model-based diagnosisis produced directly from the data that has been acquired by thewearable electronic device 100 and processed by either on-device orremote logic device 173.

From the foregoing, it will be appreciated that a machine learning modelor a hybrid of two or more such models may be used to provide additionalproactive insights into the health of a patient or other individual fromwhom data is being gathered by the wearable electronic device 100. Forexample, a machine learning model may form a classifier-based approachthat is capable of partitioning a particular condition into varioustraining classes based on the taxonomy of the condition being assessed.In one form, the condition being assessed forms the target or output ofthe classifier model, where in a more particular form suchclassification may be into one or more health conditions, such ashealthy or normal, as well as one or more anomalous conditions such asmild cognitive decline, significant cognitive decline, UTI, imminent UTIor other condition. Thus, if the concern is that a particular patient isat risk of a UTI, recursive approaches can use the taxonomy indicia inorder to generate classes of training data sets that correspond tosymptoms (as well as symptom intensity) of a UTI. From this, the output(in the form of larger inference class data sets) may be generated inorder to determine the likelihood of a UTI. Significantly, such anapproach may be done in conjunction with, or instead of, assessmentsusing traditional markers for a UTI, such as those discussed previouslyin conjunction with the NIH Unified Medical Language System. Forexample, the performance of a trained machine learning model inconducting UTI classification may include the previously-discussed ROCanalysis where the AUC is a helpful figure of merit in graphicallyevaluating the predictive accuracy of a given machine learning model.

Referring next to FIGS. 11A and 11B, a representative program structure5000 shows a hierarchical way to identify and classify various movementsthat may be used to correlate measurements taken from various forms ofsensor 121 data (such as those associated with activity sensors 121Bincluding accelerometers, gyroscopes, magnetometers or the like of FIG.2F) with certain movements that may in turn be correlated to one or moreof HAR events. Referring with particularity to FIG. 11A, various strata5100, 5200, 5300 and 5400 are shown such that broader, more sweepingactivity classifications are made within the upper strata 5100, whilemore detailed classification is shown in the successively lowerintermediate strata 5200 and 5300 to the lowest strata 5400. Forexample, within the uppermost strata 5100, a distinction betweenmovement (activity) and no movement (static) is shown, while (using the“activity” labeled classification as an example) analysis of the type ofactivity, such as whether the movement-based activity of thehighest-level strata 5100 can be more particularly identified as a fall,walking, transitional movement or something else, and from there (usingthe “transitional” category as an example) whether it involvestransition between upright positions, transition from upright to asupine position, transition from supine to upright, transition betweensupine positions or the like. From this, the attempt at a particularclassification proceeds from an understanding of more certain (but lessgranular) activities at the higher strata 5100 to less certain (but moregranular) activities at the intermediate and lowest strata 5200 through5400. In a similar manner, different classifications within a givenstrata represent activities that are understood to be independent of—andindependently measurable from—one another based on readings taken fromactivity sensors 121B.

Referring with particularity to FIG. 11B, once the activity is betterunderstood down to a fairly detailed level through the strata 5100through 5400 of FIG. 11A, there is enough semblance of order to use abinary decision approach to permit correlation of the activity sensor121B data (shown presently as accelerometer data) and its relatedmovement classification to HAR recognition. In one form, thiscorrelation may be subsequently conveyed to and displayed on the remotecomputing device 900, as well as to a machine learning algorithm inorder to correlate HAR and ADL information in order to (as shown anddiscussed in conjunction with FIGS. 14A through 14C) determine if thepatient is at risk of developing one or more adverse health conditions,as will be discussed in more detail in Section IV. For example, bydetecting static period body postures and then understanding whatdynamic activities take place between such postures allows an inferenceto be drawn about the likelihood of certain transitional movements.Likewise by differentiating between types of dynamic movements (such asthrough a frequency-based spectral analysis of the accelerometer dataand comparison to known movement thresholds, including those from thepreviously-discussed intra-patient or inter-patient baselines),additional inferences may be drawn that in turn helps in the overallclassification. Furthermore, in certain movements such as a fall analert, alarm or related message may be sent through the third wirelesscommunication sub-module 175C, gateway 300 and system 1 to the remotecomputing device 900.

In one form, context weighting may be included to allow forconsiderations of more significant portions of the data set. Likewise,data mining procedures may be undertaken in order to help inclassification activities, such as the identification of patterns withinthe acquired data; such mining may be particularly beneficial insituations where the amount of sensed or otherwise collected data islarge. Furthermore, by using machine learning, such mined data and theresulting patterns can be implemented in an automated (rather thanmanual) way. It will be appreciated that such data mining and machinelearning may be employed as a component of cognitive computing to helpextend conventional predictive analytics in order to provide CDS (in oneform) or more comprehensive diagnosis activities (in another form).Moreover, if the amount of data becomes voluminous (which may be thecase with agitation-related movement or related activity data taken overfrequent time windows each with high acquisition frequencies and lengthypatient monitoring timeframes in order to understand the data with ahigh degree of granularity and consequent finer level or more specificlevel of detail), storage of such data may be done in larger forms ofmemory 173B such as that which resides either within server 400 memory,or in the cloud 500 through the internet. In one form, daily routinesthat are common in geriatric patients would correspond to relativelygranular levels of detail. Likewise, when large, granular data sets arepresent, the chips or chipsets used to achieve various data processingwithin the logic device 173, as well as chips or chipsets used in thehybrid wireless communication module 175, may be configured withpower-efficient machine codes 173E or firmware to collect and processsuch granular data for use by a caregiver C. In one form, this logic maybe implemented in web and mobile applications. Other logic (which mayalso be embodied as machine code 173E, firmware or the like) may bedeveloped to ensure that the wearable electronic device 100 communicatesproperly with the cloud 500 to provide reliable data flow.

III. Analysis of Device-Acquired Data for ADL or IADL

Referring next to FIG. 8 in conjunction with the previously-discussedFIGS. 11A, 11B, examples are shown of how sensed data from the wearableelectronic device 100 may be used by the system 1 to better understandHAR events (FIGS. 11A and 11B) that in turn may be used to infer ADLevents, as well as how to use this improved understanding to identifychanges in the salient indicators of the health of an at-risk patient.In one form, the wearable electronic device 100 and the system 1 of FIG.1 may be used in conjunction with one another to monitor and analyze oneor more of the sensed LEAP data as evidence of HAR or ADL, and fromthere, whether such monitoring and analysis indicates a change in thehealth of the patient. In one form, machine learning may be used to helpdistinguish various ADL events, including those of a more complex,fine-grained nature from more coarse-grained ones. By way of example, anHAR-based coarse-grained understanding of an event may be the detectionthat a person P is in a sitting position, while an ADL-basedfine-grained understanding of the same event is where such sitting istaking place (for example, sitting in a chair versus sitting on afloor), as well as a more thorough understanding of the context withinwhich event takes place.

Referring again and with particularity to FIGS. 11A and 11B, arepresentative program structure according to the present disclosure mayinclude taking one or both of the HAR-related movements to correlate toADL data. The signals (such as accelerometer signals) from at least someof the intermediate strata 5200, 5300 may be subjected to low-passfiltering (such as a Butterworth filter set to an appropriate cutofffrequency) in order to produce a signal was used in certain coarse HARsensing, such as position or posture detection of the person P beingmonitored. Likewise, accelerometer signals that correlate tohigher-frequency movement and activity may be subjected to high-passfiltering in order to distinguish between static and dynamic behavior,again using an appropriate frequency cutoff. Distinctions between staticand dynamic activities may further be inferred by applying thresholds(such as g-force thresholds) to the high-pass filter results. Forexample, distinctions between posture detection and postural transitionand related movement may be determined based on the size or frequency ofaccelerometer signal variations. In one form, the static posturedetection may be based on angular components of the accelerometer signalin longitudinal and forward-facing directions, where baseline values maybe taken from for the healthy elderly population reference databases, orempirically ascertained through a sample training data set from thepresently-acquired LEAP data. The various types of dynamic motionassociated with activity, movement, postural transition or the like maybe compared to the static posture types (for example, sitting, lyingsupine or standing) as a way to detect such activity, and in one formmay be subjected to an algorithm (including a rule-based algorithm, onecapable of machine learning, as well as hybrid rule-based machinelearning (RBML)) in order to select from likely transitions between thepostures. By way of example, a transitional activity between a sittingposture and a standing posture may be equated to the movement associatedwith standing up, whereas the opposite transactional activity may beequated with sitting down. The addition of contextual information, suchas the location of the person P during these transitional activities,may be subjected to yet another algorithm (again, either a rule-basedone, a machine learning one or an RBML one) in order to infer an ADL orIADL event as will be discussed in more detail as follows.

Referring with particularity to FIG. 8, a sample patient ADLdocumentation chart is shown. This chart shows a representative fourweek period broken down by each of the days of the week along theX-axis, as well certain ADL functions along the Y-axis. In this way, acaregiver C may enter data corresponding to whether the person P beingmonitored has completed certain activities. Such a chart may be eitherin hardcopy (that is to say, paper) form, or electronic, either of whichmay be entered into a suitable electronic data file (EDF) as a datastructure into memory 173B. In one form, at least some of the LEAP datagathered by the wearable electronic device 100 and system 1 andprocessed by a suitable algorithm (such as one or more of the machinelearning algorithms discussed herein) may be entered into electronicversions of the chart of FIG. 8 to show completion of at least some ofthese indicators of ADL.

Based on the foregoing, HAR may be thought of as a more coarse orprimitive form of ADL in that the accelerometer and gyroscope data mayfirst be converted into position, movement or related activityinformation, while the addition of one or more forms of location,environment and physiological (that is to say, static) data may be addedas a way to gain contextual insight into the successively more detailedinformation associated with ADL, as well as the even more detailed,composite information associated with IADL. As is discussed elsewhere inthis disclosure, the various measurable forms of HAR may be used toinfer ADL that in turn can provide a valuable way to determine if aperson P is at risk of contracting a disease or related medicalcondition based on deviations of one or more of these factors frombaseline or related normative values. A significant barrier in applyingHAR or ADL patterns in a manner that can correlate empiricalobservations with a disease or condition prediction is the lack of largetraining sets that reflect accurate aspects of a given activity such asparticular patient movements with known predefined spaces within his orher living quarters. In particular, while there are large numbers ofsensor 121 readings to make up data input for various patient activitiessuch as the various ADLs (that is to say, the previously-discussedeating, cooking, bathing and toileting, communication, dressing,grooming, hygiene and ambulatory functions), there may be relatively fewinstances associated with a particular activity that is being classifiedor otherwise observed. While such sparse instance data normally cannotbe used to learn a complete model of an activity, it can contain enoughimportant information to correspond sensor 121 patterns to certainhighly correlative activities such as ambulatory capability or the like.In one form, filtering techniques may be used to separate importantsignals being acquired by the sensors 121 from noisy ones as a way toidentify true causal relationships from spurious ones.

In situations where the amount of LEAP data may be sparse, thehypothesis H of FIGS. 6 and 7 may be tested by using thepreviously-discussed cross-validation or bootstrapping such that theLEAP data is reused as the FIG. 6 training and validation data sets1610, 1620. By considering these factors in a machine learning context,the authors of the present disclosure have determined that in order todetermine if there is an ample supply of data for a given analysis, itmay be beneficial to determine the number of input variables or theircorresponding numerical attributes (that is to say, the features thatare the things that can characterize the object or activity in question)relative to the number of observations (that is to say, the object inquestion). Within the present disclosure, four general groups ofattributes may be measured, corresponding to the various forms of LEAPdata. For example, if there are a large number of attributes andrelatively few observations, this may be indicative of an insufficientamount of acquired data. Contrarily, once the amount of data isdetermined to be sufficient, relative errors between the testing dataset 1630 and the training data set 1610 may provide indicia of how apreliminary version of the model is performing. For example, if thetesting data set 1630 error is much higher than the error of thetraining data set 1610, the learning (that is to say, preliminary) modelmay be experiencing memorization-like overfitting, as it is having ahard time distinguishing between data and noise. Generally, theoverfitting problem is increasingly likely to occur as the complexity ofthe neural network increases. In other words, an overfitted model ismore complex than can be justified by the data. The oppositesituation—underfitting—is where the model is too simple, and as suchwill be insensitive to the actual attributes within in the data,producing predictions with poor accuracy. Moreover, the complexity ofany given model may necessitate trading off bias in the data (whichcorresponds to the expected error between a predicted (i.e., target)value and the ground truth such as that taken from thepreviously-mentioned training data set 1610) versus variance V in thedata (which measures the changes in model prediction for a given datapoint). As generalization is a measure of how well a machine learningmodel predicts outcomes for new data, a complex model thatover-generalizes has high variance because its output changes too muchbased on insignificant details about the data. This situation may beremedied by getting more training data or reducing the number ofredundant features during the algorithmic phase. On the other hand, ifthe model is suffering from high bias, acquiring more data won't behelpful as the model is already underfitting the hypothesis H. Errorsduring training decrease when the complexity of the model increases,while errors during testing decrease at first, then increase. As such,these errors can provide an indication of how any given model isperforming. The success of the machine learning models discussed hereininvolve a finely balanced trade-off between the amount of LEAP data inthe training data set 1610, the level of the generalization error (thatis to say, overfitting or underfitting) on new instances of the LEAPdata, and the complexity of the original hypothesis H that was fitted tothe LEAP data.

Referring again with particularity to FIGS. 11A and 11B, basic (that isto say, primitive) ADLs such as those that can be correlated to variousHAR may include eating, cooking, bathing, toileting, communicating,dressing, grooming, hygiene and ambulatory functions. Additional detailsfor ADL assistance may be found in The Centers for Medicare & MedicaidServices Minimum Data Set (MDS) 3.0 entitled Resident AssessmentInstrument (RAI) Manual v1.15 of Oct. 1, 2017, Section G entitledFunctional Status. In particular, a quantifiable scoring system may beused, where Table 2 shows a functional decline may involve a 0 to 4scale for an individual's self-performance of different ADLs for anactivity occurring 3 or more times and that can be compared to baselinescores for the same individual where—as shown in FIG. 6—could beembodied as baseline data 1700. Guidelines are used to quantify thescore as follows:

TABLE 2 0 Independent: no help or staff oversight at any time 1Supervision: oversight, encouragement or cueing 2 Limited assistance:resident highly involved in activity; staff provide guided maneuveringof limbs or other non-weight bearing assistance 3 Extensive assistance:resident involved in activity, staff provide weight-bearing support 4Total dependence: full staff performance every time during a 7 dayperiod

In one non-limiting example, scores may be given in various areas suchas the following from Table 3.

TABLE 3 1 Bed How the resident moves to and from lying position,mobility turns side to side, and positions body while in bed oralternate sleep furniture 2 Transfer How resident moves between surfacesincluding to and or from: bed, chair, wheelchair, standing position 3Locomotion: How the resident moves between locations a. Walk in room:how resident walks between locations in his/her room b. Walk incorridor: how resident walks in corridor or unit c. Locomotion on unit:how resident moves between locations in his/her room and adjacentcorridor on the same floor. If in wheelchair, self-sufficiency once inchair d. Locomotion off unit: how resident moves to and from off-unitlocations (e.g., areas set aside for dining, activities, or treatments).If facility has only one floor, how resident moves to and from distantareas on the floor. If in wheelchair, self-sufficiency once in chair 4Dressing How resident puts on, fastens, and takes off all items ofclothing, including donning/removing prosthesis or TED hose. Dressingincludes putting on and changing pajamas and housedress 5 Eating Howresident eats and drinks, regardless of skill. Do not includeeating/drinking during medication pass. Includes intake of nourishmentby other means (e.g., tube feeding, total parenteral nutrition, IVfluids administered for nutrition or hydration) 6 Toilet Use Howresident uses the toilet room, commode, bedpan, or urinal; transferson/off toilet; cleanses self after elimination; changes pad; managesostomy or catheter; and adjusts clothes. Do not include emptying bedpan,urinal, bedside commode, catheter bag, or ostomy bag 7 Personal Howresident maintains personal hygiene, including Hygiene combing hair,brushing teeth, shaving, applying makeup, washing/drying face and hands(excludes baths and showers)

As with ADLs, IADLs are those which require interaction with objects andpeople. Examples of such interaction may include using telephonic (orrelated) communications, shopping, meal preparation, adherence tomedication-taking protocols, money management, house-cleaningactivities, pet care, child-rearing or the like. IADLs, while notimperative for basic functioning, are indicative of the ability ofperson P to perform more complex cognitive tasks. In situations wheredata collection may be limited, some ADLs and IADLs may be deemed tobetter predict overall patient health than others, while some may bedeemed to be harder or easier to acquire. In one form, it may be helpfulto balance ease of collection versus predictive potential for thevarious ADLs. For example, directly acquiring information pertaining totaking medication, eating, ambulating and socializing may be moredifficult than others, regardless of their probative value.

One way to gain insight into HAR, ADL or IADL metrics is to firstestablish the patient baseline data 1700 (shown in FIG. 6) that can beused to establish certain activity, health or behavioral norms. Anintra-patient version of such baseline data 1700 may be created throughan experimental protocol where the patient is first equipped with one ormore wearable sensors (which in one form may include the sensors 121similar to or the same as the ones that make up the wearable electronicdevice 100) and asked to perform routine daily functions under so-callednormal conditions such as those encountered in one's home or otherfamiliar environment. In another form, an inter-patient version of suchbaseline data 1700 may be created through comparison of that patient'sactivities as acquired by the wearable electronic device 100 to those ofa larger sample population of people who share one or more traits of thepatient being baselined, such as by age, weight, gender, prior medicalhistory or the like, such as those described in the previously-discussedNIH Unified Medical Language System. Moreover, such baseline data 1700may be established for one or more events or activities, such as thoseof a single ADL, as well as from different ADLs. Such an approach maysimplify that data, including taking into consideration historic ADLstates and those that evolve over time for comparison to those of acurrent ADL state. In one form, the establishment of a baseline need notcomprise a full set of baseline data 1700, but instead merely for theparameters which are determinative of the HAR, ADL or inferred healthcondition at issue for the individual being monitored.

In another form, intra-patient versions of the baseline data 1700 may begleaned from other data forms. For example, answers to questionnairesmay be used to establish such norms. This can be used to help establishHAR or ADL baselines as well, as a battery of questions administered toa person P (such as in a clinically-controlled environment in the formof a routine office visit to his or her primary doctor or the like) mayhelp the caregiver C determine what types or quantities of LEAP data mayneed to subsequently be acquired. Using the previously-mentioned ADLmetrics (eating, cooking, bathing, toileting, communication, dressing,grooming, hygiene and ambulatory functions) as an example, a series ofquestions might involve the following:

1. Do you feed yourself?

2. Do you cook for yourself?

3. Do you bathe or shower yourself?

4. Do you use the toilet yourself?

5. Are you able to place and answer telephone calls?

6. Do you dress yourself?

-   It will be appreciated that other ADL-related questions may be used,    depending on the situation-specific needs of the individual being    monitored. In one form, these questions can be made to mimic an ADL    impairment screening questionnaire, the Katz Index of Independence    in ADL, or the like. Likewise, these and other ADL-related questions    may have a hierarchical component to them. For example, if the    patient answers “yes” to Question 4 above, a subsequent question may    involve whether the patient has any trouble controlling his or her    bladder or bowels, while other questions may relate to how many    trips per day are made to the bathroom, whether there is any pain    associated with going to the bathroom, or the like.

Once answers to these questions are known, a general ADL composite formof the baseline data 1700 may be formed. This in turn permits anindividualized tailoring of which of the subsequently-acquired LEAP datamay be most beneficial. For example, particular inferences or weightingsmay be included in order to give certain sensor 121 readings preferencein the types of acquired data that will be sent to the logic device 173.Thus, if the ADL variant of the baseline data 1700 (whether in the formof answers to questions, previously-acquired normative data or the like)raises ambulatory or mobility concerns, the data that is subsequentlyacquired through the wearable electronic device 100 for comparison maybe biased toward accelerometers, gyroscopes, magnetometers or othermotion-related ones of the activity sensors 121B. Likewise, if the ADLbaseline data 1700 raises concerns over bodily responses to certainactivity, the data that is subsequently acquired through the wearableelectronic device 100 for comparison may be biased toward thephysiological sensors 121C that may individually include the heart ratesensor, breathing rate sensor, temperature sensor, respiration sensor,pulse oximetry sensor, respiratory rate sensor, oxygen saturationsensor, electrocardiogram sensor, cardiac output index sensor,systematic pressure sensor, systematic systolic arterial pressuresensor, systematic diastolic arterial pressure sensor, systematic meanarterial pressure sensor, central venous pressure sensor, pulmonarypressure sensor, pulmonary systolic arterial pressure sensor, pulmonarydiastolic arterial pressure sensor and pulmonary mean arterial pressuresensor as discussed elsewhere in the present disclosure. In addition,and regardless of which of the sensors 121 are deemed to provide themost relevant data for a particular HAR, ADL or IADL, thesubsequently-acquired real-time data from the sensors 121 may be biasedto take place in a sequenced or connective manner based on certainpatterns of movement or behavior identified in the correspondingbaseline data 1700. Furthermore, if a caregiver C has concern that theperson P being monitored is at risk of developing a particular diseaseor health condition or the like, certain highly correlative parametersmay be used in order to bias the type of information being subsequentlyacquired by the sensors 121. This can be used in conjunction withvarious decision states that correspond to contextual situations to helpbetter inform such movement, behavior or other lifestyle activities.Thus, by combining sensory data with such contextual information, a morecomplete picture of the activity, behavior or health condition of personP may be gleaned. In one form, the contextual information may beinfrastructure-related configurational data (such as the floorplan orlayout information of the rooms in the house or other building where theindividual resides). In this way, inferences regarding unusualspatial-temporal activity data may be identified more easily. In oneform, this can be set up through various logical (“if”, “then”, “and”,“or”, “neither”, “nor”, “not” or the like) inquiries within the logicdevice 173.

As previously discussed, the system 1 may be configured to analyze thesignificance of the data either with intra-patient or inter-patientbaseline data 1700, including for algorithmic training where a data setmay be stored in local or remote memory 173B that contains classified orlabeled examples with known instances of location, movement or otheruseful biometric measures of individual activity. This training data set16010 may be input into one or more of the machine learning algorithmsdiscussed herein such that once the algorithm is optimized throughvalidation and testing of respective data sets 1620, 1630, a suitableclassification rule for use in the ensuing model is established. Thisallows presently-acquired data unique to the individual being monitoredto be input into the machine learning model in order to determinewhether the current (that is to say, real-time) activity from theindividual indicates whether the risk of a particular medical conditionis heightened.

In one form, other assessment tools, such as the Functional IndependenceMeasure (FIM), may be used to form a score of an individual to show adegree of independence based on motor and cognitive functions. The scoremay be used to assess how well an individual can be expected to meet ADLminimums. In addition, an FIM score—which may be based on a previousvisit to a physician in a doctor's office under controlledconditions—may help establish useful intra-patient baseline data 1700.Depending on the nature of the health condition, other assessment toolsor rating scales may be used to establish baseline data 1700. Forexample, if Parkinson's Disease is suspected, the Unified Parkinson'sDisease Rating Scale (UPDRS) may be used to clinically assess whether anindividual is at risk of developing the disease. It will be appreciatedthat other diseases and their scales for assessment may be correlated tosome or all of the LEAP data being acquired by the wearable electronicdevice 100, and that the inference of all such diseases through thecorrelation of one or more of their criteria with such data is withinthe scope of the present disclosure.

In one form, ADL may be modeled using Bayesian-based approaches. Ratherthan relying primarily on piezoelectric, piezo-resistive, pressure ortouch-based sensors mounted to everyday objects that could expect to behandled by the person P being monitored, or upon RFID tags, either ofwhich may have to be distributed throughout a patient's living space aspart of a complex information-gathering infrastructure, the approachdisclosed herein may take advantage of high levels of location accuracydetermination derived solely from information collected by the wearableelectronic device 100 in order to infer one or more of these ADL andIADL events. In particular, a simple cooking step such as preparing acan of soup may be inferred from the spatial proximity of the patient toobjects being used such as pots, pans, bowls, stoves or the like) or bybrushing one's teeth, which can be inferred by proximity to atoothbrush, toothpaste, bathroom sink or the like. In one form, such aninference may be made with a Bayesian belief network, where thestructure of the network is kept very simple while also relying on aslarge a number of observations related to the location, movement orother spatial or temporal indicia of a particular individual'swhereabouts or activity that in turn is used to infer a particular ADLor IADL event.

HMM variants of the Bayesian approaches may also be used in situationswhere substantial amounts of training data are needed, particularly inview of the fact that changes in the temporal segmentation of anobservation about a patient's activity may contain valuable informationabout such activity, especially because various metrics such as HAR, ADLand IADL are not directly measured but are instead inferred frompatterns in the acquired raw data from sensor 121 readings. Thepreviously-mentioned CRF is a graphical-based discriminative (ratherthan generative, such as HMI) probabilistic model that may be used as aclassification model in a manner roughly similar to Bayesian andMarkovian approaches. By being multiscale, CRF-based models areparticularly well-suited for analyzing acquired temporal (that is tosay, sequence-based) wearable electronic device 100 data, particularlyas it relates to a fusion of disparate forms of information from thevarious gyroscopes, accelerometers and other activity-measuring sensors121. The graphic-based structure of CRF models, with its featureextraction-based observational sequences (as input) and hidden states(as to-be-determined output) has the virtue of being relatively free ofbiases, particularly label bias, that may impact non-probabilisticsequence-oriented models, such as HMM and other forms of Markov models.CRF may be beneficial in situations such as compound or concurrentactivities (where, for example, the individual being monitored with thewearable electronic device 100 is talking with another while alsopreparing dinner) that may be difficult for an HMM to represent due atleast in part to a potential lack of independence in the overlappingactivities. As with some ensemble-based models, CRF may be augmented byboosting for classification problems, particularly those involving datasets that involve small numbers of data classes.

Rather than rely upon disparate sources of sensor-based dataacquisition, such as through the use of fixed, staticinfrastructure-mounted sensors such as television or closed-circuitcameras (with their attendant data accuracy, privacy and cost concerns),the sensors 121 used on the wearable electronic device 100 help improveits operability by—among other things as discussed herein—avoiding (a)the awareness by the individual of such monitoring and how that couldcause the individual to adjust his or her behavior to a degreesufficient to skew the acquired raw data, (b) the use of notoriousindicia (such as facial and voice recognition) of the individual beingmonitored that could lead to concerns that “Big Brother” is watching,and (c) the extensive amount of facility modification or retrofittingneeded to accommodate such various sensing modalities being affixed to apervasive number of objects with which the individual may interact inhis or her daily activities. For example, privacy concerns are reducedwhen the sensors 121 only acquire discrete pieces of information (suchas the fact that a patient is in the bathroom rather than the specificdetails associated with urinating or defecating), whereas morecomprehensive levels of sensed visual information detail such as camerasactually show in detail when the patient is engaged in such activitieswhile in the bathroom.

This organization of the acquired LEAP data is useful for subsequentanalytic-based operations, such as comparison of a presently-acquiredset of data to previously-acquired data from the same individual, aswell as to reference or baseline data 1700 from other individuals (suchas those in the same or similar demographic features). In one form,pre-defined threshold values may be used such that when the acquireddata exceeds or falls below the threshold, alerts may be generated. Byway of one non-limiting example, the wearable electronic device 100 orsystem 1 may send an alert for various situations that are deemed by oneor more of the machine learning models discussed herein to presentactionable changes in the health condition of the person P beingmonitored, such as unusual sleeping habits, low or high frequency ofbathroom visits (as well as excessive time spent in the bathroom), whenthe person P has fallen, when the person P is in a room or locationdeemed risky or inappropriate, where an inordinately large or smallamount of activity by the person P is detected, or ambulatory activityof the person P at unusual times (such as leaving the premises in themiddle of the night), as well as others. In one form, thepreviously-discussed geofence may be set up such that when theindividual associated with the wearable electronic device 100 wandersbeyond a designated space, the wearable electronic device 100 sends outan appropriate alert through the third wireless communication sub-module175C. Such an alert may be displayed, such as on a dashboard or relateddisplay on the one or more remote computing devices 900.

In one form, various machine learning visualization packages may be usedto facilitate easy-to-read displays on the remote computing devices 900;examples may include plotting libraries such as Matplotlib, Seaborn andPlotly, as well as custom-generated ones. Thus, in addition to storingevent data, configurable (that is to say, user-definable) parameters maybe stored in memory 173B or its system-level or cloud-level equivalentfor use with the logic device 173 and the various program structuresthat will be understood in one form to include the machine learningmodels discussed herein. One example of such configurable parameters isthat associated with how a human-machine interface (HMI) may set up onthe wearable electronic device 100, system 1 or remote computing device900 to allow interaction with a user as the caregiver C. More particularexamples of such configurable parameters include the way an ADL, IADL orHAR activity may be presented to such user, including configuring theHMI to provide customizable displays, audio alerts, thresholds or thelike. In one form, the output of an HAR, ADL or IADL study may be as adaily activity report where understanding of behavior patterns may beused for other diagnostic or treatment approaches such as forpharmacologic research to study the efficacy of medications such asanti-depressants or anti-psychotics on the individual being monitoredwith the wearable electronic device 100.

ADLs, examples of which may include eating, cooking, bathing andtoileting, communication, dressing, grooming, hygiene and ambulatoryfunctions, are good indicators of the cognitive and physicalcapabilities of a person P being monitored. In one form, assessment ofADL may be performed by one of various models, such as those based onthe approach pioneered by Sidney Katz, MD and entitled Index ofIndependence of Activities for Daily Living, where the sensed, measuredand related collected LEAP data can be used in conjunction with machinelearning that may be present within system 1. In one form, thismulticlass raw LEAP data may be correlated to ADL in a manner thatallows inference-drawing that mimics the conventional observationalapproach taken by Katz. The present use of LEAP data and machinelearning is advantageous in that it avoids the need for expert systemsand their control programs that make extensive use of a priori rules(often numbering into the thousands) to produce meaningful conclusionsin a real-time setting. This is not to say that expert systems may nothave any utility; in fact, placing such resources into a configurationused by cloud 500 could take advantage of its parallel processingconfiguration to provide analytics and related clinical decisioninsight. For example, RBML such as learning classifier systems,association rule learning and artificial immune systems may act as ahybrid between purely rules-based approaches and purely machinelearning-based approaches. More particularly, patient interventionstrategies enabled by the collection and analysis of the data discussedherein by the wearable electronic device 100 and system 1 may help toidentify problematic changes in health when such changes are imminent oremerging, rather than after the onset of an illness or other adversehealth condition. In one form as will be discussed in conjunction withFIG. 15, the LEAP data may help to identify acute or crisis stages earlyin their development to allow proactive rather than reactiveintervention.

As will be understood within the present disclosure, both inter-patientand intra-patient historical (that is to say, previously-acquired)information may be used to establish baseline data 1700 that may in turnbe used for comparison to real-time (that is to say, presently-acquired)data such as the LEAP data acquired from the wearable electronic device100. In this way, such comparisons may form the basis for determiningthat an individual's present activity, location or condition has changedrelative to an accepted norm by an amount sufficient to warrant furthercaregiver C inquiry or intervention. Moreover, LEAP data, bothhistorical and present (i.e. real-time) may be used for some or allforms of data analysis, including to build and test a machine learningmodel, while real-time or presently-acquired LEAP data may be operatedupon by the model in order to perform one or more of CDS, diagnosis orthe like. Thus, in one form, the LEAP data may serve as an exclusivebasis for both baseline data 1700 and real-time data, while in anotherform, the LEAP data is used for just one or the other. As such, allforms of acquiring and using the LEAP data from the wearable electronicdevice 100 in order to evaluate a health condition of an individual inthe manner discussed herein will be deemed to be within the scope of thepresent disclosure.

Algorithmically, various forms of sensed data from the wearableelectronic device 100 may be stored in memory 173B, processed andanalyzed by one or both of the system 1 and the wearable electronicdevice 100. As discussed previously, such data includes LEAP data.Examples of location data (which may have spatio-temporal components)include (1) time spent in the bedroom, bathroom, neighbor's room, diningarea, entertainment/activity area, courtyard, garden, outside, inside orthe like, (2) time spent out of a particular room (such as the bedroom),(3) number of bathroom visits or any other room per day, (4) number oftimes outside or any within other room, (5) number of times outsidewithin a particular location, such as to smoke in a known or designatedsmoking area, (6) cumulative number of rooms visited, (7) a certainamount of time in the bathroom or any other room, (8) cumulative amountof time and duration spent within certain rooms, and (9) specific timespent within any other particular place of interest (such as a roomwhere exercise equipment may be present or where physical therapy mayroutinely be administered). In one form, some or all of this data may bestored as part of an activity index to provide a measure of thelocomotor or related physical activity of a person P; such an index mayexist as a data structure in various formats, such as a lookup table orthe like. From an understanding of this data, various intervention plansmay be implemented. For example, a smoking cessation program may bedeveloped based on analysis of the LEAP data that shows that the personP being monitored is making frequent visits to indoor or outdoorlocations that are known to include designating smoking zones. Inaddition to using the LEAP data for analyzing the behavior of the personP wearing the wearable electronic device 100, by tracking the responseof caregivers C in a hospital, assisted living facility or other placewhere multiple persons are being cared for, such as to a requestinitiated by the person P through the nurse call button 131, caregiver Cactivity and behavior may be more closely tracked.

Examples of activity data include (1) the number of activities attended,(2) the types of activities, (3) the amount of time spent in suchactivities, (4) the previously-mentioned activity index, (5) an amountof time inside of the patient's bedroom, as well as the number of roomsthe patient may have visited, (6) how many times the nurse call button131 was utilized, as well as the time of day nurse call button 131 waspressed and the amount of time it takes to clear the nurse call inaddition to the identity of a staff member who cleared the nurse call,(7) the number of times the patient gets up in the middle of the night,(8) the number of times the patient gets up in the middle of the nightand uses the bathroom, (9) the number of times the patient eats in thedining hall versus in their room throughout a week or other measurableunit of time, (10) the amount of time a patient is sitting, standing ormoving, and (11) gait speed. In one form, the activity data may be madeup exclusively of changes in location data over time, where the greaterthe change in location or a reduction in the amount of time to achievesuch change could be indicative of a greater activity level. Conversely,a smaller amount of location change or a greater amount of time requiredto achieve such change could be indicative of a lower activity level.Similar use of environmental parameters (for example, temperature,humidity, air pressure, carbon monoxide, carbon dioxide, smoke or thelike) and physiological parameters (for example, heart rate, breathingrate, temperature, respiration, pulse oximetry, blood sugar monitoring,respiratory rate, oxygen saturation, electrocardiogram, cardiac outputindex, systematic pressure, systematic systolic arterial pressure;systematic diastolic arterial pressure, systematic mean arterialpressure or the like may also be performed in order to gain a morecomplete understanding of an individual's activity within a givenspatio-temporal context. Such information may also be stored in memory173B for use in the analysis of an individual's health condition; forexample, such information may be stored as numerical values of therespective category of the LEAP data that is being acquired. In oneform, because one or both of the location and activity data havespatio-temporal components, it may be beneficial to treat individualframes of sensor 121 data as statistically independent, isolatedportions of the input data such that through suitable extraction suchdata may be grouped into a feature vector format for subsequent use by aclassifier or other machine learning algorithm or model.

In one form, the sensors 121 that are used as part of the wearableelectronic device 100 and system 1 for combining and transmitting datarelated to ADL may be configured to acquire information associated witha specific activities (for example, to sense drawer opening, dooropening, toilet flushing, weight load on a toilet seat, faucet use orthe like), where the sensors 121 are capable of transmitting ADL StateRelated Data (ASRD). In a similar manner, the sensors 121 may be used toprovide even relatively small changes in measured environmentalparameters such as atmospheric pressure that can provide indicia ofvariations in height or elevation through activities such as climbingstairs, or changing between a sitting, standing and supine positiontraversing or the like. In addition, sensor 121 sampling rates (such asonce per second or more) may be varied in order to acquire fine changesin the barometric pressure. In a similar manner, variations intemperature (such as going between indoors and outdoors) and humidity(such as being near the source of a faucet, bathtub, toilet or othersource of running water) may be acquired. In this form, machinelearning-based supervised classification may be used for successfullydistinguishing these and other subtle changes in detailed ADLparameters. In one form, these and other parameters may be used topresent a more thorough understanding of the patient's immediateenvironment. As previously mentioned, the sensed data may have atemporal component that in turn may be used to facilitate the machinelearning process to conduct time series analysis or forecasting., suchas through Bayesian or neural network approaches. Numerous examples ofdata that may have a temporal component include those associated withthe LEAP data discussed herein, where more particularly, ambulatoryactivity may be acquired by one or more sensors 121 over a time sequencein order to divine speed, duration or changes in such activity. Temporaldata associated with ADL may also include—in addition to timestamping—frequency of occurrence, duration of occurrence, elapsed timebetween occurrences, running averages of occurrences or the like. In oneform, the measurement of the temporal data helps in establishing norms(such as those that may form part of an inter-patient or intra-patientbaseline data 1700). This indexing of the data over the time dimensionis valuable in helping to identify HAR traits, patterns or the like thatin turn may be correlated to certain ADL and IADL markers that in turncan be used to assess the ambulatory and cognitive capability of apatient based on the data being collected by his or her wearableelectronic device 100. As with other forms of acquired data, thetemporal data may be subjected to a feature extraction process in orderto allow comparison of potentially disparate pieces of information. Forexample, because various activities may be performed over periods oftime that are compared to the sampling rate of the sensors 121, it maybe beneficial to recognize such activities over one or more time-sampledsliding windows. Because the received data is unlikely to be identical(even for the same individual performing the same activity), it may behelpful to use statistical or structural filters in order to transformthe raw data into a set of feature vectors for each given window. Forexample, statistical-based feature extraction may be used on rawactivity data, while structural-based feature extraction may be used onthe raw environmental data.

In such form, the system 1 may also include one or more BLE beacons 200capable of broadcasting Beacon Related Data (BRD) in order to establishthe location of the specific ADL. The wearable electronic device 100 iscapable of receiving and storing BRD from a BLE beacon 200 and ASRD fromthe ADL sensor to establish the time at which the BRD and ASRD arereceived. In one form, the wearable electronic device 100 (1) combinesthe data related to the identity of the individual, time of reception,ASRD and BRD with the UUID in a manner suitable for RF transmission; (2)transmits the combined data to the gateway 300 or server 400; (3)optionally adds handshaking steps to verify data transmission using theUUID; (4) optionally adds steps that ensure that only data receivedwithin a specific time duration are combined; and (5) integrates signalactivities from different types of beacons or tags that can trigger thewearable electronic device 100 to perform different functions, internalfunctioning or events the results of which may be pushed to the cloud500 or internet through system 1. In addition, encryption or relatedsecurity protocols may be included to ensure that the storage andtransfer of Health Insurance Portability and Accountability Act(HIPAA)-compliant patient-identifying data stays protected.

EXAMPLE

The results of a trial implementation (i.e., study) of location patternmodels is described next. The purpose of the study was to determine thefeasibility of using location pattern models to monitor changes inmovement patterns as an approach to reduce the fall rate of assistedliving facility patients. The results of the study are based on dataacquired with the wearable electronic device 100 to identify potentialchanges in health, as well as to reduce or prevent patient falls (suchas previously discussed in conjunction with FIGS. 11A and 11B) at homeor in an assisted living facility, LTC, skilled nursing facility, memorycare unit or the like. Not only are falls and associated instabilityharmful in and of themselves, they also provide indicia of poor health,a chronic health condition, or decline in ambulatory status or cognitivefunction. Through the monitoring of movement patterns with the wearableelectronic device 100 and other parts of the system 1, there is apotential to detect early changes in acute conditions such as UTI,pneumonia, agitation, medication side effects (including orthostatichypotension, bradycardia or the like) as well as changes in gait, all ofwhich are potential predictors of a fall. As previously mentioned,changes in gait could be tied to spatial navigation difficulties. In oneform, a portion of the baseline data 1700 may be in the form ofcognitive maps that can provide indicia of spatial navigation skills inorder to provide early detection of a dementia-related health condition.In one form, fall detection may be achieved by (1) having movement beacquired by the sensors 121, (2) sending acquired data to the gateway300 that in turn sends the data to the cloud 500 where it is analyzed byan API to distinguish normal movement from fall movement. In addition,the application server 420 may generate and send reports and alerts onthe fall, as well as the location of the patient, to a family member orother interested caregiver C through the remote computing device 900. Inanother form, abnormal movement that is not indicative of a fall, suchas coupling certain movement with unusual times of the day, may be usedto provide indicia of certain rhythms (such as circadian activity) thatcould indicate sleeplessness, changes in mental status or the like.Moreover, certain movements (such as can be detected by activity sensors121B) that are understood to describe fall precursors may be analyzed inorder to correlate them to change in chronic obstructive pulmonarydisease (COPD) status, changes in oxygen saturation or other signs ofimminent adverse health conditions.

The study was also conducted to determine whether residents and staff ofan assisted living or related health care facility would be accepting ofthe wearable electronic device 100, as well as of the practicality ofusing alerts related to changes in movement patterns in determining achange in health condition, potential fall risk or other information ofinterest to caregivers C. The design methodology included using severalapproaches. For example, a single cohort pre-post design was used toevaluate the use of the location pattern models in reducing falls. Inaddition, a descriptive design was used to address the acceptability andpotential for implementation; this design was verified through the useof interviews and focus groups.

Ten assisted living facility residents at different facilities in theChicago, Illinois area were identified as being at-risk for falls at onefacility, and these residents formed the participant sample. Thesefacilities have the capability to provide various services along theelderly care spectrum, including assisted living, long-term care (LTC)and memory unit capabilities. Location data was collected twenty fourhours a day for two months, by residents having the wearable electronicdevice 100 affixed to their wrists. Patterns were monitored throughautomated detection and logging software. Alerts for a change inmovement pattern were sent to facility management staff in situationswhere follow-up was deemed to be necessary. Facility management staffwas also able to explore data further through detailed reports ofresident patterns and behavior. During the study, movement patternchanges were based on: (1) the number of bathroom visits by theresident; (2) the number of times outside of the resident's normal room;(3) the amount of time the resident spent in the bedroom; (4) the numberof times the resident gets up in the middle of the night; (5) the numberof times the resident gets up in the middle of the night and uses thebathroom; (6) the amount of time the resident is moving and not moving;and (7) the amount of time spent in activity areas by the resident.

The results of the study and the data acquired for a single one of theresidents showed patterns of behavior as follows. Initially, during thebeginning of the week, the resident appears to stay in the facility, yetby Wednesday, the resident left the facility three times. This issignificant as the data shows that this behavior is beyond theresident's usual (that is to say, baseline) pattern of walking aroundthe facility parking lot. Additionally, the data showed that theresident was most active in the morning. As the day went on, theresident spent more time in the bedroom or—during the afternoon—thelibrary. Lastly, the data showed that on Wednesday of the week oftesting, the resident's other activity decreased dramatically.Additional outcomes to be measured in a subsequent study may provideindicia of (1) resident fall rate before and after the implementation oflocation pattern models or, as will be discussed in conjunction withFIG. 14C, the implementation of or changes in a medication regimen; (2)description of alerts (number, type, pattern) per study participant andper group; (3) description of characteristics of the participantsassociated with falls and with alerts; (4) using a geofence to follow anitinerant resident outside of the facility where he or she is residingand (5) the feasibility and usability of the wearable electronic device100 from participant and staff perspectives. Based on this exemplarystudy, the potential exists to identify early changes in health statusthrough tracking of activity or movement patterns by using system 1 withthe wearable electronic device 100 as disclosed herein. In oneparticular form, the ability of one or both of the wearable electronicdevice 100 and system 1 to store and review data and real-time alerts ina post-event situation may be used to conduct a root cause analysis thatin turn may serve as a predictor for future preventable events. With theroot cause analysis using the LEAP data, causes for the failure of aprevious treatment regimen may be uncovered. For example, correlationsbetween a new or changed medication regimen and patient falls (thelatter as evidenced by the LEAP data) may be uncovered. In one form ofthe example, a patient started taking a new medication at 8 AM everyday, and that for the next three days, the patient had a fallingepisode, whereas that same patient's baseline data showed that prior tothat, falls were happening no more than once a week. Other scenarios andexamples of how analysis of these and other forms of LEAP data may bepresented to a caregiver C, such as being displayed on a screen of theremote computing devices 900, as described next in conjunction withFIGS. 9 and 10A through 10F.

IV. Clinical Decision Support and Diagnosis of Health ConditionsIdentified with Device-Acquired Data

The use of the wearable electronic device 100 to collect and transmitLEAP data may be better understood with recourse to two broad categoriesof health conditions, namely (A) infections and (B) neuropsychiatricconditions, as well as (C) other common ones.

Referring next to FIG. 9 in conjunction with FIGS. 10A through 10F, aparticular example of using the system 1 to collect data in order toidentify changes in salient indicators of an at-risk patient (such asone with dementia, delirium or the like) is shown, such as for a UTI(FIG. 9), activity (FIGS. 10A through 10E) or agitation (FIG. 10F). Moreparticularly, an example of the system 1 in operation is shown in FIG.9, where a notional assisted living community, nursing home or relatedmulti-patient facility is projected as an image on a display of orscreen 910 of the remote computing device 900. In the non-limitingexample shown, each patient room may be outfitted with a single BLEbeacon 200. One or more BLE beacons 200 may be placed in a common areaor perimeter. The display on the remote computing device 900 forcaregivers C (including family members of those with health care ordurable power of attorney) may be used to further demonstrate theinfluence of, or need for, care interventions. For example,incorporating this data into family care conferences may assist theoperator of a facility in deciding what services the facility will beexpected to provide for the patient in the future or as part of achanged care plan.

Referring with particularity to FIGS. 10A through 10F, various chartsused to put the acquired LEAP data into user-friendly display format areshown to allow a caregiver C to determine whether a person P may be atrisk of developing an adverse health condition such as an infection,pneumonia, sepsis, cognitive deficit, neuropsychiatric conditions (whichmay include agitation) or the like. All of the charts of FIGS. 10Athrough 1OF are generated by the wearable electronic device 100 andsystem 1 of FIG. 1 and allow a caregiver C to determine person Pspatio-temporal data as part of a time series analysis in order tocorrelate changes in the health condition of person P over a certainincrement of time.

In one form, these charts may be displayed on one or more of the remotecomputing devices 900. For example, FIG. 10A shows a daily time andactivities chart 3000 that identifies a particular patient 3100 as wellas planned activities 3200 that in one form may be tailored to theparticular needs of patient 3100. A time chart of patient 3100 engagedin pacing 3300 is shown to provide ready graphical indicia of periods ofsuch pacing. Monthly activity circle charts 3400 and monthlyexit-seeking activities 3500 are based on LEAP data that is generated bythe wearable electronic device 100, where the latter may be a measure ofwandering and elopement tendencies. FIG. 10B shows a daily bathroomvisit chart 4000 in bar graph format; such information allows acaregiver C to readily determine whether a patient 4100 may be at riskof developing a UTI based on bar chart 4200 showing the number ofbathroom visits per day. Up/down arrows give ready graphical indicia ofactivity changes per week 4300, while a comparable chart of the amountof time spent in the bedroom per day 4400. FIG. 10C shows a daily roomfrequency chart over the course of a notional week made up of the15^(th) through the 21^(st) of a given month; from this, irregularpatterns of visits to certain rooms may be readily identified. FIG. 10Dshows a daily performance in bar chart form of the frequency of roomvisits over the period of a notional week to help track an individualfrom room to room, as well as show where the individual spends his orher time on a daily basis. As such, FIGS. 10C and 10D may be readtogether to provide not only the number of times a monitored patientgoes into a particular room, but also the amount of time spent in suchroom, which can further help to identify unusual trends or patterns.FIG. 10E shows a geolocation chart where walks taken by a monitoredindividual outside a home H or related assisted care facility, nursinghome, apartment or related dwelling of varying distances on threeseparate days may be used to infer peripatetic tendencies.

From tracked data, the wearable electronic device 100 will enable thefollowing metrics to assist in measuring outcomes. First, the time spentin the bedroom per day 4400 may be used to show decreased nighttimebedroom use and nighttime wakefulness both of which may indicateescalation of behavioral symptoms. Likewise, increased daytime bedroomuse may indicate depression and apathy symptoms, as well as the onset ofinfection or worsening of a known medical comorbid condition. Second,time spent in primary activity areas by daily quantity (for example,minutes versus hours) may be collected to show evidence ofneuropsychiatric symptoms. Third, the previously-mentioned elopement andwandering episodes of the exit-seeking activities 3500 may be helpful inproviding an indication to a caregiver C that additional patientoversight or wandering mitigation strategies may be needed. In addition,episodes of use of the nurse call button 131 by weekly quantity and timeof day may provide indicia of anxiety. Moreover, time of day data may behelpful if associated with medication administration and side effects(examples: orthostasis associated with antihypertensive agent,bradycardia associated with antiarrhythmic, increased anxiety ordiscomfort prior to next medication dose timing affecting frequency ofadministration, increasing dyspnea or shortness of breath associate withworsening of co-morbid conditions) an example of which was discussedpreviously relating a new medication regimen and increases in thefrequency of falling episodes. Likewise, special circumstances dictatedby facility research intervention protocols as necessary may also beimplemented.

The goals of effective management would include early detection ofongoing comorbid conditions, symptom relief and reduction of caregiver Cdistress. Management strategies can be influenced by active locationdata collection and evaluation. Algorithms include early detectionchange in a health condition associated with the onset of infectionsincluding UTI and pneumonia (frequently associated with episodes ofaspiration), detecting escalating behavioral episodes of agitation andanxiety, monitoring for depression and apathy patterns of behavior,monitoring for signs of worsening comorbid conditions includingcongestive heart failure (CHF) and COPD with decreased activitypatterns, and quantitating activity change after initiation of treatmentprograms tracking the influence of pharmacological andnonpharmacological interventions.

Machine learning models in general, Bayesian and neural networks inparticular and even more particular types of time series-basedfeedforward neural networks (such as the previously-discussed LS™networks that are part of a recurrent deep neural network, randomforests and gradient boosting approaches) may help predict upcomingconditions based on previous-in-time (that is to say, temporal) datasuch as that associated with accelerometers and gyroscopes. Likewise,some of these approaches, such as random forests and gradient boosting,provide relatively high degrees of accuracy while also avoiding thetendency to over-fit that can be common with other decision tree-basedapproaches. In one form, LSTMs and related time series models may bebeneficial in analyzing at least location and activity data that couldinclude historical aspects of the acquired data from the wearableelectronic device 100. For example, an LS™ may be used to predict futureelements of a given sequence based at least in part on such historicalinformation. LSTMs may also be used in situations where informationassociated with multiple input variables is being acquired. In one form,LSTMs work best when there is a large amount of data being acquired.Likewise, in situations where location data plays a relativelysignificant role in analyzing a person P with the wearable electronicdevice 100, clustering-based approaches such as the previously-mentionedK-means clustering may be particularly beneficial, particularly when theamount of data being acquired is relatively small.

Referring with particularity to FIG. 10F, raw pacing data from a pilotprogram is shown. In the pilot program, physical therapists used a gymroom with which to simulate a notional living space LS with individualliving room LR, dining room DR and kitchen K in order to understand howa person P to be monitored moves within such an environment. Data wascollected every few seconds over a period of one day, where the timemarkers M representing stationary behavior for more than three minutes.In one form, the time markers M may be used in conjunction with thelinear distance L between sequential readouts of the time markers M todetermine agitation-related parameters such as speed of movement, gaitpattern, frequency of movement, time of day of movement or the like.This data, which in one form is acquired through the signals transmittedfrom the BLE beacons 200 to the first wireless communication sub-module175A, may then be analyzed to determine if these movement-relatedactivities can be correlated to pacing and patient agitation. Within thepresent disclosure, the term “pacing” is meant to distinguish normallevels of back-and-forth movement such as (a) within a room occupied byother people where the individual being monitored may be socializing,(b) changing the channels of a television, (c) preparing a meal in thekitchen K from those movements, particularly linear back-and-forthmovements within a single room. In one form, the time markers M may beused to provide indicia of escalation or de-escalation of behavioralsymptoms from a baseline value, as well as help a caregiver C evaluatethe efficacy of interventions including both pharmacological andnon-pharmacological interventions.

Referring next to FIGS. 14A through 14C in conjunction with FIGS. 9through 10F, and in a manner roughly analogous to inferring HAR, ADL orIADL as discussed in conjunction with FIGS. 11A and 11B, the use ofmachine learning in the analysis of the various forms of data acquiredby the wearable electronic device 100 may be employed to detect patternsin medical data that due to their complexity, volume or both wouldotherwise be difficult or impossible. By analyzing these patterns andpresenting them to doctors, advanced practice clinicians, nurses andother caregivers C, a machine learning or other cognitive-simulatingmachine calculation can use such acquired data to identify changes inthe salient indicators of the health of an at-risk person P.

As mentioned above, the early detection of indicia of adverse healthconditions such as a UTI, pneumonia or other infection, neuropsychiatricconditions, as well as other common conditions, in addition to earlymedication-based intervention or related treatment of any of theseconditions, can help prevent avoidable hospitalizations orre-hospitalizations.

(A) Infections

Early identification of a change in condition (CIC), also referred to asan acute change of condition (ACOC), is a key factor in decreasingmorbidity and rate of hospitalization or re-hospitalization,particularly for infection-prone, frail and elderly residents ofassisted living facilities, nursing homes and related facilities.Significantly, patients with cognitive impairment and the onset ofdelirium (which is defined as confusion above a baseline and which inturn is related to the development or worsening of an illness, such asthrough sepsis and systemic inflammatory response syndrome (SIRS) thatarises out of an individual's immune system response to such infection)may be unable to provide the information needed to help a caregiver Cascertain whether the person P meets one or more specific infectioncriteria. In such case, the wearable electronic device 100 data—as wellas the analytics that can be generated by the system 1—can provide thisnecessary information on a real-time basis and alert the caregiver C ofthe early onset of ACOC. In addressing a change in functional status ofa person P, the data being acquired by the wearable electronic device100 and the accompanying analytics can provide an inference of suchchange through metrics such as the activity index that can provide aquantitative measurement of locomotor activity as a way to correlatesuch activity to impairments brought about by changes in healthconditions such as an infection. In one form, the activity index mayinclude or be used as part of a baseline with which to comparepresently-acquired movement, position and related activity data in orderto assess whether significant changes in locomotor capacity of person Pis present.

Although shown in FIG. 9 as having a single BLE beacon 200 in bathroomBR, it will be appreciated that if more fine-grained detection isneeded, there may be multiple such BLE beacons 200 arranged in variousplaces within the bathroom BR in order to improve the spatio-temporalnature of the data being collected; this may be particularly beneficialin situations, where such data can provide to doctors, nurses and othercaregivers C indicia of ADL (such as that gleaned from the datarecording and analyzing activities associated with FIGS. 8, 11A and 11B)that in turn can provide advance warning of undesirable changes in thehealth of a patient, such as a UTI in the case of data being derivedfrom the bathroom BR. In one form, when used in a machine learning modeof operation, the system 1 of the present disclosure is able to learn toextract features, as well as to be trained to identify patterns from thedata that arises from ADL events in an experiential and ad hoc way (thatis to say, without the need for the algorithms or models that are beingused to analyze the data to be comprised entirely of a fixed set ofprogram code). As previously mentioned, such learning may be supervisedor unsupervised, depending on the needs of the particular healthcondition being analyzed.

As previously mentioned, one of the most significant contributors toavoidable hospitalizations or re-hospitalizations of patients pertainsto infections in general and the high incidence of UTIs in particular,especially for the elderly and those suffering from dementia. In oneform, the system 1 for UTI detection combines time-based data frombathroom visits and toilet flushes combined with other data acquiredfrom the wearable electronic device 100. Additional information, such asthat acquired from the facility schedule of events (such as thatdepicted in FIG. 10A) may be used, as well as other external data, suchas that associated with the time of the year, weather or the like.Comparisons can be made from intra-patient or interpatient baseline data1700 and subjected to machine learning analysis in an attempt toidentify patterns or significant deviations from the norm. For example,a machine learning-based approach may be used in conjunction with thewearable electronic device 100 or the system 1 in order to acquire firstand second (i.e., baseline and present) data about an individual so thattwo or more different states or related health conditions (as embodiedin a data structure in memory 173B) may be compared based on theacquired first and second data. Various criteria may be used, an exampleof which is the Milliman Criteria that set forth targets in order tohelp individual patients attain certain outcome metrics following anytype of intervention or procedure (such as surgery).

Referring with particularity to FIG. 14A, a program structure 6000 isshown in the form of a flow diagram of how one or both of the wearableelectronic device 100 and system 1 of FIG. 1 may be used to help acaregiver C determine if the person P being monitored is at risk ofdeveloping a UTI. As previously mentioned, a baseline condition mayfirst be ascertained, as shown in event 6100. In one form, this mayinclude the collection of movement patterns and the subsequent analysisto determine HAR, ADL or IADL, as discussed previously in conjunctionwith FIGS. 11A and 11B. From this an alert in event 6200 may begenerated (such as by logic device 173) if a significant deviation fromthe baseline-established a norm is detected. In the non-limiting exampleshown, a significant deviation may be a greater than 35% change in oneor both of bathroom visit frequency and time spent in the bathroom overa daily period. Additional analysis—such as in the form of a decisiontree—may be used depending on whether the quantitative change evidencesand increase (event 6300) or decrease (event 6500) relative to thebaseline. For example, if the change indicates an increase in thesebathroom-related events, the subsequent event 6300 may includeevaluating for UTI symptoms such as quantitative changes in the activityindex, body temperature or the like, as well as qualitative changes inlocal pain, urine character or the like. Likewise, if the changeindicates a decrease in these bathroom-related events, the subsequentevent 6500 may include evaluating for increased incontinence. If theincrease in bathroom-related events and subsequent evaluation event 6300indicates negatively, then event 6400 may be used to consider evaluationfor other issues such as gastrointestinal causes, medication effects orthe like, whereas if the increase in bathroom-related events andsubsequent evaluation event 6300 indicates in the affirmative, then acaregiver C may infer that the McGeer Criteria for a UTI has been met,after which a report event 6800 may be generated for consideration ofone or more medical intervention activities. Likewise, if the changefrom event 6200 indicates a decrease in these bathroom-related eventsand the evaluation for increased incontinence event 6500 may indicateeither positively or negatively, additional events may be undertaken,such as evaluating the person P for other conditions 6600 such asdehydration, urinary retention, medication effects or the like insituations where incontinence is no in evidence. Contrarily, insituations where increased incontinence may be present, the evaluationevent 6300 may again be undertaken in a manner similar to situationswhere the alert from event 6200 does indicate an increase compared tothe baseline 6100. Depending on this other conditions 6600 inquiry, thesubsequent events 6400, 6700 and 6800 may further be undertaken.

One particularly useful set of diagnostic, treatment, and surveillancecriteria for UTI analysis—particularly for nursing home patients—is theMcGeer Criteria, while another is known as the Loeb Criteria thatprovides a consensus of localizing symptoms that in one form may beviewed as certain updates to the McGeer Criteria. Within the context ofthe present disclosure, it is recognized that both criteria differ insome respects, but also exhibit a lot of similarity. As such, it will beunderstood that the term “McGeer Criteria” is used herein as a shorthandfor either approach for early identification of a UTI. In one form, thecriteria may be patient-centered (that is to say, clinical), while inanother, the criteria may be population-based (that is to say,surveillance). Relatively recent updates to the McGeer Criteria added aso-called Constitutional Criteria, particularly as it relates toresidents of LTC facilities. In addition to revising some of theprevious criteria, it also added leukocytosis as one of the criteria, aswell as set forth additional ADL-related factors of what must be presentfor changes in functional status, such as acute change in mental statusand acute functional decline. For example, an acute change in mentalstatus diagnosis may be met by (1) an acute onset (that is to say, a newor worse condition from a baseline), (2) fluctuating behavior (that isto say, behavior that comes or goes or experiences changes in severity),(3) inattention (that is to say, a difficulty in focusing or inabilityto maintain attention) and (4) at least one of (4A) disorganizedthinking (that is to say, thinking that is hard to follow or doesn'tmake sense) and (4B) altered level of consciousness (that is to say,sleepy, unarousable or lethargic).

Presumed UTIs are the most common reason that antimicrobials areprescribed for older adults. Thus, by implementing the McGeer Criteriausing machine learning from data acquired through the wearableelectronic device 100 and analyzed on system 1 as a way to identifywhether a patient meets criteria for evaluation—and possible reductionin the likelihood—of developing a UTI prior to the patient developingsymptoms may help to reduce the need for antimicrobial medications orother treatment. More particularly, the McGeer Criteria may differslightly depending on whether or not the patient is using a catheter.The McGeer Criteria as presently implemented for UTIs of patientswithout an in-dwelling catheter includes two major criteria both ofwhich must be present. The first major criteria (1) requires: at leastone of (a) and (b), where (a) is acute dysuria or acute pain, swelling,or tenderness of the testes, epididymis, or prostate and (b) is fever orleukocytosis (as the so-called Constitutional Criteria), and furtherrequires at least one of the following localizing urinary tractsub-criteria made up of (i) acute costovertebral angle pain ortenderness; (ii) suprapubic pain; (iii) gross hematuria; (iv) new ormarked increase in incontinence; (v) new or marked increase in urgency;and (vi) new or marked increase in frequency. Likewise, in the absenceof meeting the fever or leukocytosis of the first major criteria, apositive diagnosis of a UTI may also require the presence of two or moreof (i) suprapubic pain, (ii) gross hematuria, (iii) new or markedincrease in incontinence, (iv) new or marked increase in urgency and (v)new or marked increase in frequency. The second major criteria (2)requires a positive urine culture in the form of one of the followingsub-criteria: (a) at least 10⁵ colony-forming units per milliliter(CFU/ml) of no more than two species of microorganisms in a voided urinesample, and (b) at least 10² CFU/ml of any number of organisms in aspecimen collected by in-and-out catheter.

In situations where the patient is using a catheter (often referred toas a catheter-associated symptomatic UTI scenario), two (slightlydifferent) criteria must be satisfied. The first major criteria (1)requires one or more of the following with no alternate source: (a)fever; (b) rigors; (c) new onset of hypotension with no alternate siteof infection; (d) new onset of confusion or functional decline alongwith leukocytosis; (e) new costovertebral angle pain or tenderness; (f)new or marked increase in suprapubic tenderness; (g) acute pain,swelling or tenderness of the testes, epididymis or prostate and (h)purulent discharge from around the catheter. The second major criteria(2) requires (if the urinary catheter removed within last two calendardays): (a) a voided urine culture with ≥10⁵ CFU/ml of no more than twospecies of microorganisms and (b) positive culture with ≥10² CFU/ml ofany microorganisms from straight in/out catheter specimen. Likewise, thesecond major criteria (2) requires (if the urinary catheter is in place)a positive culture with ≥10⁵ CFU/ml of any microorganisms from anindwelling catheter specimen.

The 2012 updates to the McGeer Criteria removed change in character ofurine and worsening of mental or functional status. Nevertheless, arough equivalent to the latter is now present in the form of ADL-relatedConstitutional Criteria where mental status change or acute functionalstatus decline are determined, such as through the MDS 3.0. For example,the mental status change may be present if all of the following aredetected: acute onset, fluctuating course, inattention and disorganizedthinking or altered levels of consciousness. Likewise, acute functionalstatus change may be present if there is a three-point decrease in ADLscore based on seven ADL items including (1) bed mobility, (2) transfer,(3) locomotion within the LTC facility, (4) dressing, (5) toilet use,(6) personal hygiene and (7) eating.

In one form, analyzing whether an individual is at risk of developing aUTI includes applying machine learning logic, such as that associatedwith one or more of the machine learning models discussed herein, to theacquired LEAP data taken from the wearable electronic device 100. In oneparticular form, this may include using at least some of the machinecodes that are stored on memory 173B of either the wearable electronicdevice 100 or the backhaul server 400 in conjunction with the respectiveprocessor 173A to execute at least a portion of the McGeer Criteria. Forexample the machine code may include that which executes an analysis todetermine one of the criteria mentioned above particular examples ofwhich may include the new or marked increase in urination urgency, thenew or marked increase in urination frequency and the new or markedincrease in incontinence. Significantly, these are among the criteriathat can be measured either directly or indirectly (that is to say,inferred) through the sensors 121 and hybrid wireless communicationmodule 175 and the resulting LEAP data. As such, directly-measuredlocation properties, such as identification of a patient as being withinthe bathroom BR of FIG. 9 or other particular room, along with temporaldata, such as how long or how frequently such patient is in the bathroomBR, as well as what time of the day the patient is in the bathroom, inconjunction with activity data, such as how rapidly or slowly thepatient moves to and from the bathroom BR, and in addition tophysiological data that may show heartrate, body temperature, excessshaking or the like, may be used to infer or predict a likelihood thatthe patient is suffering from—or is at risk of developing—a UTI. In oneparticular form, the measured values may be compared against acceptednorms such as through contemporaneous or previously-acquired historicalbaseline data 1700, as discussed elsewhere in the present disclosure, asa way to increase the accuracy with which such inference or predictionis made.

Another common form of infection that plagues the elderly and thosesuffering from cognitive impairments is pneumonia a version of which isreferred to as community-acquired pneumonia (CAP) that is a leading formof infectious disease that leads to patient hospitalization. Certainforms of the LEAP data may be particularly probative of an individual'slikelihood of contracting pneumonia. For example, the location data mayprovide indicia that an individual has spent time in a location wherethe likelihood coming into contact with another who may have pneumoniais heightened. Similarly, activity, along with respiratory and otherphysiological data, may provide indicia of speed of movement,respiration rate, heat rate, shallowness of breathing or the like thatcan be correlated to the likelihood of pneumonia.

As with UTIs, pneumonia (which in one form is a subset of the largergroup of maladies known as infections) may use similar criteria for CDSor diagnosis. For example, one form of output used for diagnosis or CDSof a pneumonia may require that all of the following conditions besatisfied: (1) a positive chest X-ray for either pneumonia or newinfiltrate; (2) one or more of (a) new or increased cough or sputum, (b)reduced oxygen saturation (as well as a 3% decrease compared to abaseline), (c) abnormal lung exam new or changed (d) pleuritic chestpain or respiratory rate of greater than 25 breaths per minute; and (3)evidence of one or more constitutional criteria (that is to say, fever,leukocytosis, acute change in mental status from baseline and acutefunctional decline). Regarding pneumonia-specific conditions, there arewell-established criteria for community-acquired pneumonia (CAP) andhealthcare-associated pneumonia (HCAP). For example, CAP may furtherinclude minor criteria (such as high blood urea nitrogen levels,confusion or disorientation, hypotension, hypothermia, leukopenia,multilobar infiltrates, partial arterial oxygen pressure to fraction ofinspired oxygen ratios, respiratory rates, thrombocytopenia) and majorcriteria (such as invasive mechanical ventilation or septic shock withneed for vasopressors). Particular examples of diagnosis codes relatedto pneumonia may be found in the International StatisticalClassification of Diseases and Related Health Problems (ICD) codes suchas ICD10 examples of which include ICD10 001-139 (infectious diseases),ICD10 460-529 (respiratory system), as well as others.

There are various scoring systems to help determine if a patient haspneumonia, including the Pneumonia Severity Index (PSI) that consists oftwenty clinical and laboratory parameters and is recommended by theAmerican Thoracic Society (ATS)/Infectious Diseases Society of America(IDSA). Another scoring system is referred to as the CURB-65 score. TheCURB-65 score is for a series of risk factors including (1) Confusion ofnew onset (defined as an abbreviated mental test score (AMTS), where ascore of 7-8 or less suggests cognitive impairment), (2) blood Ureanitrogen greater than 7 millimoles per liter (or 19 mg/dL), (3)Respiratory rate of 30 breaths per minute or greater, (4) Blood pressureless than 90 mm Hg systolic or diastolic blood pressure 60 mm Hg or lessand (5) Age 65 or older. CURB-65 simplifies the scoring system comparedwith PSI, but may reduce sensitivity for other pneumonia indicia, suchas one referred to as a measure of death occurring within 30 days of ahospital admission known as 30-day mortality. Yet another score, knownas the SMART-COP score (for Systolic blood pressure, Multilobarinfiltrates, Albumin, Respiratory rate, Tachycardia, Confusion, Oxygenand pH) may be used. Still another approach, called (A-DROP) usesfactors such as Age, Dehydration, Respiratory failure, Orientationdisturbance and systolic blood Pressure, where the dehydration componentis understood to be a common manifestation of an infection associatedwith decreased intake, as well as increased needs associated with afever.

The authors of the present disclosure believe that certain physiologicaldata as acquired by the wearable electronic device 100 may provide abetter indication of a patient's true status, as well as provide abetter prediction of such patient's outcome. For example, for thesensors 121 mentioned previously, certain physiological parameters, suchas respiration rate, heart rate, unusual breathing patterns (such aswheezing or the like) and temperature, as well as the patient's locationand activity (such as measured by one or both of the first and secondwireless communication sub-modules 175A, 175B, as well as the sensors121 configured as accelerometers, gyroscopes, magnetometers or the like)may be analyzed (such as by one or more of the machine learning modelsdiscussed herein) in order to provide a statistically-significantindication that the measured values of one or more of the variouslocation, environment, activity and physiological parameters mean thatan adverse health condition of the person P being monitored is imminent.In one form, the LEAP data acquired by the wearable electronic device100 may be used as a supplement to one or more of the PSI, CURB-65,SMART-COP or A-DROP scores as a way to provide a higher degree ofconfidence or weighting that one or more of the parameters contributingto a pneumonia score is present.

As with the UTI as described above, in one form, analyzing whether anindividual shows predictors of pneumonia onset versus risk fordeveloping the illness may include applying machine learning logic tothe acquired LEAP data. In particular, patterns arising from aparticular combination of patient location, movement (or relative lackthereof) using the first and second wireless communication sub-modules175A, 175B and physiological condition (using one or more of the sensors121), possibly in conjunction with ambient environmental conditions(also using one or more of the sensors 121) allows for a data-drivenpredictive analytic approach to infer the likelihood of a pneumonia evenabsent direct clinical measurement of a patient's condition, such asthrough the conventional PSI, CURB-65, SMART-COP or A-DROP scoreapproaches. In another form, the data may be used as a way to supplementsuch score-based symptom information.

Relatedly, the wearable electronic device 100 may be used to gain anunderstanding of the likelihood of an influenza outbreak. Influenza (ormore commonly, the flu), which is defined as a highly contagious viralinfection of the respiratory passages causing fever, severe aching, andexcessive discharge or buildup of mucus in the nose or throat resultingwith inflammation of the mucous membrane, is indicated by varioussymptoms that may be inferred from analysis of at least a portion of theacquired LEAP data in a manner analogous to UTIs and pneumonias. Some ofthe symptoms include fever, breathing difficulty, chills, headache, achymuscles, cough, nasal congestion, fatigue and sore throat. Sensors 121configured to detect temperatures, excessive vibratory activity (such asthat associated with severe coughing, rapid heartbeat, deep or laboredbreathing, among others) may be used to particularly ascertain unusuallevels of physiological activity. As with the UTI and pneumonia, many ofthese symptoms may be ascertained from the previously-discussed sensors121 that are configured with physiological data-acquiring capability.

Moreover, as with the UTI and pneumonia described above, analyzingwhether an individual shows predictors of the onset of influenza mayinclude applying machine learning logic to the acquired LEAP data todivine patterns arising from patient location, movement andphysiological condition, possibly in conjunction with ambientenvironmental conditions, where the determination of whether the diseaseis either present or imminent may be made absent direct clinicalmeasurement of the patient's condition through one or more of thesymptoms that may in one form be specific to influenza. As with theother forms of infection, the data may be used in conjunction withscore-based information such as that taken from a direct interactionbetween the patient and his or her physician.

(B) Neuropsychiatric Conditions

As with infections, criteria used to infer a neuropsychiatric conditionof an individual associated with the wearable electronic device 100 maybe based upon accepted disorder classifications, such as those found inthe Diagnostic and Statistical Manual of Mental Disorders, 5^(th)edition (DSM-5) that uses a disease coding system that corresponds withcodes from ICD10. Such diagnostic classification may include anxietydisorders, bipolar and related disorders, depressive disorders,disruptive, impulse-control and conduct disorders, dissociativedisorders, elimination disorders, feeding and eating disorders, genderdysphoria, neurocognitive disorders, neurodevelopmental disorders,obsessive-compulsive and related disorders, paraphilic disorders,personality disorders, schizophrenia spectrum and other psychoticdisorders, sexual dysfunctions, sleep—wake disorders, somatic symptomand related disorders, substance-related and addictive disorders andtrauma- and stressor-related disorders. If not diagnosed properly or intime, symptoms associated with one or more of these disorders can leadto serious persistent mental illness (SPMI), where failure to managesuch may exacerbate other forms of disease progression that may in turncontribute to excess morbidity and mortality.

Referring next to FIG. 12, a neurocognitive disorder of particularrelevance to the present disclosure is dementia, which may be furtherclassified as having major or mild variants. As such, while this andrelated cognitive impairments may be considered a psychiatric conditionunder DSM-5, within the present disclosure, such impairment may becategorized as a subset of a neuropsychiatric condition, or as aseparate health condition, where either variant is deemed to be withinthe scope of the present disclosure, regardless of its semanticclassification. Moreover within the present disclosure, while the terms“psychiatric” and “neuropsychiatric” imply different subsets ofoverlapping medical disciplines (specifically, psychiatry andneurology), they are treated interchangeably within the presentdisclosure for the purpose of correlating the various forms of LEAP dataas acquired by the wearable electronic device 100 to certaincorresponding adverse health conditions. Furthermore, the various formsof the LEAP data can be used as indicia of behavior or patterns (forexample, increased patient agitation) that in turn can be attributed toor at least correlated with such conditions through changes in acceptedpatient norms irrespective of how one or more conditions—particularlythose dealing with changes in mental status—are categorized undercommonly-accepted medical diagnosis codes. For example, if an analysisbased on the acquired LEAP data from the wearable electronic device 100indicates that a patient is experiencing increased agitation, suchincrease may be either (a) an early warning sign of an imminent changein health condition (such as infections, dementia or otherneuropsychiatric conditions) or (b) an indication of other adversechanges in health (including infections) where a diagnosis or suspicionof dementia or other neuropsychiatric conditions is already in place.Thus, regardless of whether a particular health condition is a malady inand of itself or merely the byproduct of another, the LEAP data acquiredfrom the wearable electronic device 100 can be used, along with suitableanalysis such as from the machine learning approaches discussed herein,to help caregivers C mitigate the effects of such condition. Forexample, a caregiver may be able to more readily identify when theperson P being monitored is in an agitated or upset state even absentoutward manifestations of such agitation.

In one form, agitation may be correlated to one or more neuropsychiatricconditions, including schizophrenia with increased negative symptoms(such as isolation, de-socialization or the like), dementia withpsychosis and bipolar disorder or other conditions where antipsychoticmedication is frequently prescribed. Within the present disclosure, theterm “agitation” refers to a range of behavioral disturbances includingaggression, combativeness, disinhibition, hyperactivity, shouting,pacing and exit-seeking. Moreover, even though agitation is semanticallymentioned as being associated with or a subset of a psychiatric orneuropsychiatric condition, including dementia and associated cognitiveimpairments or declines, agitation may also be construed as astand-alone condition in that the source of such agitation may be froman as-yet undiagnosed underlying adverse health condition (such as UTIs,delirium and other conditions). It will be appreciated that at allvariants, irrespective of how they are grouped, are within the scope ofthe present disclosure.

A diagnosis of dementia (such as that which can identify whether apatient is in an early, moderate, late or terminal stage) requires thatat least two core mental functions be impaired enough to interfere withdaily living. These mental functions include ability to focus and payattention, ability to reason and problem-solve, language skills, memoryand visual perception. Dementia symptoms—some of which are shown inconjunction with FIG. 12—may include cognitive changes such as confusionand disorientation, difficulty communicating or finding words,difficulty handling complex tasks, difficulty reasoning orproblem-solving, difficulty with planning and organizing and difficultywith coordination and motor functions memory loss. Dementia symptoms mayalso include psychological changes such as the previously-discussedagitation, as well as anxiety, depression, hallucinations, inappropriatebehavior, paranoia and personality changes. Moreover, dementia may comein many forms, including Alzheimer's disease, frontotemporal dementia,Lewy body dementia, mild cognitive impairment (MCI), mixed dementia andvascular dementia, while additional diseases with dementia-like symptomsmay include Huntington's disease, traumatic brain injury,Creutzfeldt-Jakob disease and Parkinson's disease. In addition toidiopathic etiologies, causes of dementia may include anoxia, braintumors, high blood pressure, infections and immune disorders, lack ofphysical and social activity, metabolic problems and endocrineabnormalities, normal-pressure hydrocephalus, nutritional deficiencies,poisoning, reactions to medications, smoking, subdural hematomas,unhealthy dietary habits and vitamin D deficiencies.

The decrease in the functional status of person P as dementia progressesalong the timeline may be grouped into various stages using a dementiatrajectory chart where the functional status extends along the Y-axisand the timeline extends along the X-axis. In one form, dementia may bebroken down into an early stage, a moderate stage, a late stage, aterminal stage and ultimately death. In the early stage (typicallybetween one and two years after a diagnosis), relatively small anomaliescan be noticed, including lack of initiation of activities, confusionabout places and times (including arrival at an improper location at animproper time) and loss of love of life. Personal items tend to getlost, and the person P acts more withdrawn. Certain ADL or IADL-relatedevents, such as a decline in skill sets, inability to manage money orinability to provide personal care for one's self are often observed.Irritability and suspicion of others is often a sign of the later partsof the early stage. In the moderate stage (typically between two and tenyears after a diagnosis), the functional status declines more to thepoint where heightened levels of care may become necessary, includingfull time supervision and assistance with mobility, heightened problemswith reading, writing and performing calculations, as well as making upstories in order to fill in the increasingly frequent gaps in memory.Telltale signs within this stage may include loss of impulse control,emotional lability, restlessness, sloppiness, outbursts of anger,frequent sleeping, nighttime wandering, incontinence of urine, childlikebehavior, paranoia, diminished social activity, increased fall frequencyof falls as well as falls in attempting to transfer from one place orposition to another. At this stage, assisted living may be required forpersonal care, which may subsequently progress into nursing homeplacement. In the late stage (which may occur between one and threeyears after the moderate stage), telltale signs may include the patientbecoming almost completely non-ambulatory, having poor safety awareness,increased rate of mental decline (particularly after an acutehospitalization event), forgetting when his or her last meal was eaten,little capacity for self-care, requires help with all bathing, dressingand eating activities, loss of bowel control, be prone to making verbalutterances not related to pain, increased amount of sleeping, difficultyin communicating with words, difficulty with liquids, coughing aftertaking a drink, lack of appetite and weight loss even with a good diet.In the terminal stage (which typically lasts no more than about sixmonths), the patient experiences recurrent aspirations even with thickliquids, pressure ulcers even with frequent turnings and related goodquality of care, as well as unawareness of external stimuli. Death oftenoccurs as a result of sepsis, pneumonia, UTI or other infection. Delaysin the diagnosis of these conditions may contribute to increasedmorbidity and mortality.

Referring next to FIG. 13, the neuropsychiatric symptoms of dementiaaffect individuals across all stages and etiologies. Furthermore, theretends to be a strong correlation between dementia and agitation. Infact, in the middle and later stages of the illness, as many as 50% ofpatients with dementia will exhibit agitation, while 70% will experienceepisodes of psychosis within the first six or seven years of theillness. In addition to agitation, symptoms may include aggression,depression, anxiety, delusions, hallucinations, apathy anddisinhibition. Of these, agitation particularly appears to demonstrate ahigh degree of correlation with activity, where one approach known asthe Cohen-Mansfield Agitation Inventory (CMAI) the short form of whichis shown, possibly in conjunction with the Mini Mental State Exam(MMSE), may be used to not only correlate agitation and activity, butalso assess how cognitive function may be correlated to activity. Inanother exemplary form, mean motor activity (MMA, such as measurablewith an actigraph) can be used to correlate CMAI scores. For example,individuals identified as having a high agitation status may be inferredthrough the use of MMA and a related strong correlation with CMAI totalscores in general and more detailed verbal agitation and non-aggressivephysical agitation scores in particular from the CMAI. Another scale,referred to as the Pittsburgh Agitation Scale (PAS), is often brokendown into four behavior groups for psychogeriatric analysis; these fourgroups include aberrant vocalizations, motor agitation, aggressivenessand resisting care. Significantly, many components of these fourbehavior groups may be based on parameters that may be directly sensedby the wearable electronic device 100 such that the results can populatea CMAI table or related data structure. For example, data collected froman audio variant of the sensors 121, as well as the location or movementvariant of the sensors 121, all as disclosed herein, may be used to drawinferences about levels of patient agitation. In one form, the dataacquired from the wearable electronic device 100 is used as part of apredictive model to determine impending agitation such that an alertedcaregiver C has the opportunity to intervene through a primary outcomeor secondary outcome action plan, care plan or the like before the useof psychotropic drugs (such as antidepressants, antiepileptics,anxiolytics, antipsychotics, and anticonvulsants) or hospitalizationbecomes necessary. In one form, various types of data may be collectedin order to establish a correlation between movement-based activity andagitation. This data includes (1) intra-room and room-level movementdata, (2) heart rate data, (3) PAS data and (4) qualitative observationdata. Likewise, data that corresponds to sleeping patterns may also beused in order discern agitation or other changes in mental status. Asmentioned elsewhere, the raw data can be cleansed and transformed intousable measurement data for further analysis.

In one form, the collection of various forms of LEAP data with thewearable electronic device 100 may be deemed to be a form of digitalphenotyping when used in conjunction with a machine learning model inorder to diagnose a psychiatric or neuropsychiatric condition withoutrecourse to canonical classification approaches such as those associatedwith DSM-5. For example, one or more features may be extracted from theacquired LEAP data using linear (for example, short-time Fouriertransform) or non-linear (such as fractal dimension) functions forsubsequent use by a suitably-trained classification, regression orreinforcement model. In such form, the wearable electronic device 100and machine learning model cooperate with one another as a device toconduct computational psychiatry, where the acquired LEAP data may beused to mathematically describe a patient's cognition in sufficientdetail in order to correlate a representation of one or more psychiatricor neuropsychiatric conditions to the symptoms being observed throughthe data.

Through patterns in the LEAP data acquired by the wearable electronicdevice 100, the caregiver C may gain additional insight into whether aperson P is at risk of developing an avoidable neuropsychiatriccondition. For example, repetitive or obsessive movements by the personP being monitored, particularly when used in the context of a particularlocation or time of day and baseline data 1700, may be analyzed by themachine learning models discussed herein to provide indicia of thepresence of such condition. Other patterns, examples of which includechanges in sleep patterns, faster or slower mobility, pacing, fidgetingor the like may also be identified through analysis of the LEAP data.This in turn may allow a physician to better ascertain the efficacy ofany medication being administered, as well as whether to adjust aparticular dosage of such medication, in addition to whether amedication regimen should be commenced or terminated. As will bediscussed in conjunction with FIG. 14C, the LEAP data may be used tohelp a physician perform a geriatric medication evaluation (alsoreferred to as a geriatric medication algorithm) where activity-relatedinformation pertaining to a patient's gait, falling tendency, wanderingtendency, level of agitation, restlessness or the like can help provideanswers to an algorithmic series of questions used to determine theappropriateness of a particular medication regimen. Early detection ofescalations in behavioral symptoms would be expected to improve efficacyof interventions and decrease adverse consequences. The LEAP datadiscussed herein contributes to the understanding of the need for amedication regimen, as well as the efficacy of specific dosageadjustments. This last decision support is particularly beneficial inlight of statistics gathered over the last few decades that has shown asignificant increase in the percentage of the adult population that istaking at least one prescription drug, as well as the number of adultstaking three or more prescription drugs. A 2016 paper presented to theAmerican Hospital Association noted that the total net spending onprescription drugs is almost $310 billion annually, making prescriptiondrugs the fastest growing segment of the U.S. healthcare economy, andmore importantly that a significant fraction of this money is going towaste as up to half of the 3.2 billion prescriptions written in the U.S.each year are not taken as directed, if even taken at all. This in turnleads to over $250 billion dollars of unnecessary costs, or roughly 13percent of the country's total annual healthcare expenditures.

As with the various forms of infections described above, analyzingwhether an individual is at risk of an adverse neuropsychiatriccondition may include applying machine learning logic to the acquiredLEAP data to determine whether any patterns can be gleaned arising frompatient location, movement and physiological condition, possibly inconjunction with ambient environmental conditions. As with theinfections, such a determination may be made without recourse to directclinical measurement of the patient's condition. Also as with thevarious forms of infection discussed herein, the data may be used incomparison against baseline data 1700 such as that taken from a directinteraction between the patient and his or her physician, as well asfrom known norms of the patient or a similarly-situated group ofpatients with similar demographic or health condition attributes. Forexample, the baseline data 1700 may be in the form of a score-basedcriteria such as that of the previously-mentioned CMAI, PAS or the likein a manner analogous to the McGeer Criteria, PSI score, CURB-65 score,SMART-COP score, A-DROP score used for UTIs, pneumonia and otherinfections.

Autism is another form of neuropsychiatric condition that can at leastbe partially identified by LEAP data acquired with the wearableelectronic device 100. In one form, the length of time that a student iswilling to remain relatively motionless while in a classroom setting maybe determined by one or more forms of the LEAP data, including thelocation data acquired by the first and second wireless communicationsub-modules 175A, 175B, as well as activity data acquired from the groupof activity sensors 121B. Such information may include a temporalcomponent, so that such lengths of time may be compared against baselinedata 1700. For example, if a norm is 45 minutes, and the individual ismanifesting signs of movement at 30 minutes, then an indication ofinattention may be inferred.

Referring with particularity to FIG. 14B, a program structure 7000 isshown in the form of a flow diagram of how one or both of the wearableelectronic device 100 and system 1 of FIG. 1 may be used to help acaregiver C determine if the person P being monitored is at risk ofdeveloping a worsening psychiatric condition. As with the UTI analysisof the program structure 6000 that was previously mentioned inconjunction with FIG. 14A, a baseline condition may first beascertained, as shown in event 7100. From this an alert in event 7200may be generated (again, such as by logic device 173) if a significantdeviation from the baseline-established a norm is detected. In thenon-limiting example shown, a significant deviation may be a greaterthan 35% change in a real-time activity index relative to a baseline.Additional decision tree-like analysis may be used depending on whetherthe quantitative change evidences and increase (event 7300) or decrease(event 7400) relative to the baseline. For example, if the changeindicates an increase in the real-time activity index, a subsequentevaluation may be for things such as the effect of a medication regimen,changes in psychiatric symptoms (including anxiety, agitation or thelike), infectious causes, positive effects from prior therapy orrecovery from a previous illness. Likewise, if the change indicates adecrease in the real-time activity index, a subsequent evaluation inevent 7400 may be for things such as the effect of a medication regimen,changes in psychiatric conditions such as depression, infectious causesor dehydration. Regardless of whether the inquiry in event 7200 isindicative of an increase or decrease in real-time activity, event 7500may be used to assess the person P in order to obtain vital signs, O₂saturation, as well as to observe for subtle signs and symptoms, afterwhich event 7600 may be used to report a change in the health conditionof the person P being monitored, as well as event 7700 for an evaluationof the person P or consideration of one or more medical interventionactivities. As previously mentioned, certain activities may beindicative of agitation or other anomalous behavior, and the earlyidentification of agitation to allow the caregiver C to take correctiveactions may be used to fend off a worsening condition.

Referring with particularity to FIG. 14C, a program structure 8000 isshown in the form of a flow diagram of how one or both of the wearableelectronic device 100 and system 1 of FIG. 1 may be used to help acaregiver C determine the efficacy of a medication regimen for a personP who has been determined by the program structure 7000 of FIG. 14B tohave a neuropsychiatric condition. In one form, activity-related dataacquired by the wearable electronic device 100 can provide input that acaregiver C can use as part of an evaluation algorithm. In one form,such an algorithm may be used to reduce the extent of the medicationregimen (called polypharmacy in situations where the regimen involvesprescribing numerous different types of medications), as well asmitigation strategies where the medication regimen is deemed to beinappropriate and in need of change, such as the use of psychotropicmedications in nursing home, assisted living or LTC facilities. In amanner analogous to using the LEAP data to establish a correlationbetween a new or changed medication regimen and an increased incidenceof person P falling as discussed previously, the LEAP data may be usedto correlate an inappropriate patient-specific medication regimen and anincreased incidence of neuropsychiatric conditions. For example,evidence of outbursts, excessive wandering, pacing or movement-relatedagitation that can be gleaned from the LEAP data may be used to inform acaregiver C of the need for an intervention strategy such as a changedmedication regimen or the like.

As with the UTI analysis of the program structure 6000 that waspreviously mentioned in conjunction with FIG. 14A, a baseline conditionmay first be ascertained, as shown in event 8100, particularly as itrelates to things such as a current list of medications, current bloodpressure or the like. From this an evaluation 8200 of each of themedications from the baseline may be conducted through a battery ofdecision tree-like questions, including whether there is a specificindication 8210 for the drug in question, the feasibility ofdiscontinuing the drug 8220 (in the event that there is no indicationfrom the previous inquiry), and if so to discontinue it 8230, or if notto ascertain whether a less toxic alternative 8240 might be appropriate,at which time either a substitute drug 8250 or a reduced dosage of thepresent drug 8260 might be appropriate. Contrarily, if the initialevaluation inquiry pertaining to a specific indication 8210 for the drugin question returns a yes answer, then a battery of risk 8270 questionsmay be asked to find out either the feasibility of discontinuing thedrug 8220 or decrease in dosage 8280, at which time the decision to seeka reduced dosage of the present drug 8260 might be undertaken. After theevaluation 8200 of each of the medications is completed, an evaluation8300 of the entire drug regimen may be conducted where—depending onwhether there is a concern over drug interactions, side effects or thereis a possibility of simplifying the regimen, a recommendation 8320 toprioritize drug dosages, schedules or preparations may be generated. Inaddition, an inquiry into whether the person P is willing or able tocomply 8400 with medication-taking protocols can be pursued. Inparticular, once a battery of questions 8410 pertaining to the abilityor willingness of a person P to comply are answered, the caregiver C maybe in a better position to provide education 8420 in the form of writteninstructions, ordering a home health evaluation or the like.

As with the prior program structure 6000 of the UTI analysis, the use ofthe logic device 173 and its ancillary circuitry and components may beimplemented in order to automate the process or take advantage of eitherrule-based algorithms or machine-learning based models with which tohelp with either CDS or a diagnosis in and of itself. From there, andalso as with the prior program structure 6000 of the UTI analysis, theLEAP data being acquired from the wearable electronic device 100 may beused to provide actual environmental, activity or physiologicalconditions or events associated with the person P being medicated, aswell as providing context in order to determine if such conditions orevents are within acceptable limits or outside the norm. With regard tothe context, other subtle signs of activity data, such as changes ingait, speed of movement, agitation, pacing or the like may also be usedto help provide a more holistic picture of the health condition ofperson P, as can data associated with the ambient environment orlocation and data that provides physiological context such as bodytemperature, heart rate or the like.

(C) Other Conditions

Referring next to FIG. 15, a generalized model of chronic disease isdepicted, where functional status decline over time shows a relativelycommon downward trajectory to that of the dementia timeline chart ofFIG. 12. Within the present context, the trajectory model of chronicdisease may be applied to the analysis of various health conditions suchas congestive heart failure (CHF), chronic obstructive pulmonary disease(COPD), smoking cessation, as well as the previously-discussedneuropsychiatric condition, among others. Regardless of the chronicdisease being analyzed, some generally identifiable phases PH along thetrajectory may correspond to changes in the functional status, includingan initial (or pre-trajectory) phase PH₁ where no signs or symptoms arepresent, a trajectory onset phase PH₂ where an initial onset of no signsor symptoms may be detected and where a diagnostic period may commence,a crisis phase PH₃ where a potentially life-threatening situationarises, an acute phase PH₄ that follows the crisis phase PH₃ and wheresigns or symptoms may be controlled by a prescribed regimen, a stablephase PH₅ that begins once signs or symptoms are controlled by theregimen, an unstable phase PH₆ where signs or symptoms are notcontrolled by the previously-adopted regimen, a downward phase PH₇ whereprogressive deterioration in mental and physical health is present and adying phase PH₈ that correspond to the weeks, days or hours precedingdeath. It will be appreciated that the representation of the phases PH₁through PH₈ as shown are generalized, and that greater or lesser numbersof such identifiable phases PH may be present depending on the chronicdisease being analyzed.

In one form, the use of the LEAP data from the wearable electronicdevice 100 may be particularly beneficial in CHF acute exacerbationscenarios to help overcome the traditional lack of general consensus onthe best way to screen for asymptomatic ventricular dysfunction. Forexample, a CHF analysis might progress through various stages thatinclude an early stage, an established disease stage, an advanced stageand an end stage that correspond generally to three of the identifiablephases PH along the trajectory. For example, at an early stage (whichmay generally correspond to trajectory onset phase PH₂), a person P mayexhibit shortness of breath, swelling in lower extremities, weight gainor experience trouble sleeping. In this stage, symptoms tend to flare upsuch that the disease worsens with each episode. Once the person Preaches the established disease stage (which may generally correspond tophases PH₃ through PH₅), other symptoms may include ongoing cough orwheezing, lack of appetite or nausea, as well as rapid or irregularheartbeat. Next, when an advanced disease stage (which may generallycorrespond to phases PH₆ through PH₇) is reached, increased or racingheartrate, confusion, impaired thinking or the like may be noticed, aswell as chest pain, increased anxiety, weight gain or loss, anasarca oredema leading to diuretic adjustments, decreases in albumin level orincreases in brain natriuretic peptide (BNP) levels. The end stage(which may generally correspond to phase PH₈) may include constant chestpain, tiredness and weakness, breathlessness, difficulty walking,decreases in sodium level, anemia, evidence of kidney shutdown,refractory fluid overload or ascites. In ICD situations at this stage,there may be discussions with the patient or family member regardingturning off devices that are artificially prolonging life, palliativecare or hospice options. In one form, a program structure in the form ofa flow diagram similar to the ones of FIGS. 14A through 14C may be usedto show how one or both of the wearable electronic device 100 and system1 of FIG. 1 may be used to help a caregiver C determine if the person Pbeing monitored is at risk of developing CHF, where correspondingquestions, decision points and recommended responses specific to CHF aresubstituted for the ones pertaining to an infection or neuropsychiatricissue.

In a manner analogous to CHF, COPD stages may include an early stage, anestablished disease stage, an advanced stage and an end stage thatcorrespond generally to three of the identifiable phases PH along thetrajectory. For example, at an early stage (which may generallycorrespond to trajectory onset phase PH₂), a person P may exhibitrepeated bronchitis, increased mucus production or occasional shortnessof breath. As with CHF, in this stage the symptoms tend to flare up suchthat the disease worsens with each flare up. Once the person P reachesthe established disease stage (which may generally correspond to phasesPH₃ through PH₅), tightness in the chest, chronic coughing, frequentbronchitis, shortness of breath when walking, so-called “barrel” chestand pursed-lip breathing are often in evidence. Next, when an advanceddisease stage (which may generally correspond to phases PH₆ through PH₇)is reached, manifestations may include constipation, sleeplessness andfatigue, poor appetite, increased pain and anxiety levels, shortness ofbreath with simple tasks, weight loss, orthopnea, sudden waking eventsaccompanied by shortness of breath, increases in so-called “air hunger”,limitations in activity, confusion and steroid dependence leading tocomplications. The end stage (which may generally correspond to phasePH₈) may include severe limits on activity, shortness of breath whiletalking, lack of appetite, depression and other events that may prompt afamily member or caregiver C to initiate conversations about palliativecare or hospice options. In one form, a program structure in the form ofa flow diagram similar to the ones of FIGS. 14A through 14C may be usedto show how one or both of the wearable electronic device 100 and system1 of FIG. 1 may be used to help a caregiver C determine if the person Pbeing monitored is at risk of developing COPD, where correspondingquestions, decision points and recommended responses specific to COPDare substituted for the ones pertaining to an infection orneuropsychiatric issue.

It will be understood from the present disclosure that as with CHF andCOPD, mental health diseases such as the neuropsychiatric conditionsmentioned previously may extend along a trajectory that may include anearly stage, an established disease stage, an advanced stage or latestage that correspond generally to three of the identifiable phases PHalong the trajectory. For example, at an early stage (which maygenerally correspond to trajectory onset phase PH₂), a person P mayexhibit a flat affect, lack of pleasure in everyday life, cognitive ormemory problems, thought disorders, inability to function due to theirbehavioral issues (i.e., mania), sleep disturbances, aggression oragitation. As with CHF and COPD, in this stage the symptoms tend toflare up such that the disease worsens with each flare up. Once theperson P reaches the established disease stage (which may generallycorrespond to phases PH₃ through PH₅), hallucinations, delusions,decreases in speech, substance abuse, as well as ongoing sleepdisturbances, aggression and agitation are often in evidence. Next, whenan advanced disease stage (which may generally correspond to phases PH₆through PH₇) is reached, manifestations may include incontinence,dependency upon others for ADL (often accompanied with an inability tocare for one's self), weight loss, ongoing sleep disturbances,aggression and agitation, as well as the need for repeatedhospitalizations. Life expectancy with these diseases may involve a tento twenty five year reduction due to one or more of the lifestyleattributes of the person P with a mental health condition. For example,a person P with one or more mental health problems may have a higherrisk of physical illness due to: an increased tendency to be obese andsmoke; the ongoing use of antipsychotic medications; increased risk ofstroke, myocardial infarction and life threatening arrhythmias, amongothers. These phases

While the general trajectory for all of these conditions is a downwardworsening in functional status that ultimately ends in death, it is thepresence of various acute, crisis or unstable stages along thetrajectory that are of most interest to the present disclosure and theLEAP data being acquired by the wearable electronic device 100 foranalysis on it or the system 1. Thus, in one form, dips D associatedwith certain acute events may signal transitions between subsequent onesof the identifiable phases PH along the downward trajectory. Using anacute event occurring at the crisis phase PH₃ (or established diseasestage) from the CHF as an example, at least some of the acquired LEAPdata may be analyzed (such as by one or more of the machine learningmodels discussed herein) in order to identify condition changesproactively at the threshold PH_(3t) of these dips D (rather thanretroactively at the end PH_(3e)) so that suitable medical interventionmay be undertaken in enough time in order to avoid or mitigate theeffect of these acute events. In one form, this may be part of a diseasestate management (DSM, also referred to as disease management) protocolto allow a holistic approach to healthcare monitoring, intervention andcommunication for a person P who exhibits one or more of these healthconditions.

In one form, the previously-mentioned activity index provides a totalscore for an individual's activity level such that when compared againsta baseline index (such as that which may form part of baseline data1700), changes in status (including functional status such as thatdepicted in FIGS. 12 and 15) are readily identified through comparisonof presently-acquired LEAP data and the activity index baseline. In oneform, the acquired data that is being output from the wearableelectronic device 100 through the third wireless communicationsub-module 175C may include those previously mentioned, including thepreviously-discussed location data related to time spent in the bedroom,number of times going to the bathroom, or the like. In one form, theactivity index may be preserved in memory 173B such that it describes acumulative physical activity of the person P being monitored. In suchform, the memory 173B may work in conjunction with the processor 173A toprovide updates to the values stored in the activity index.

(D) Assessment of Data Acquired by the Wearable Electronic Device andCommunication of a Diagnosed Health Condition

Regardless of the health condition being analyzed, there is a need forthe caregiver C or other user to draw an inference based on the acquireddata. For example, increased incidents of drinking and toileting may beindicative of increases in bladder function, which in turn may be linkedto a UTI. Likewise, significant increases in pacing or other itinerantbehavior may portend an increased likelihood to wander, as well asprovide indicia of bipolar disease, dementia with psychoses or otheragitation-related conditions. Similarly, evidence of CHF or COPD may begleaned from some of the acquired physiological data, possibly inconjunction with the location, activity or environmental data. In oneform, diagnostics about these and other conditions may be more closelycorrelated with identifying variations from the norm rather than thepatient's activity viewed in the abstract. In one form, this can be doneby comparing present patient activity with an established baseline fromexisting data, including that from representative sample or demographicgroups. In such cases, the use of a representative data set, in somecases along with the balanced, low-bias data set previously-discussed,may be employed. In another form, individualized baselines may also beused, as deviations from expected values may vary drastically fromperson to person. In this situation, acquiring data for each patientthrough the location-based functionality of the hybrid wirelesscommunication module 175 and one or more of the sensors 121 of thewearable electronic device 100 facilitates the rapid formation ofindividualized baselines. Therefore, when such baseline data 1700 iscompared to a particular, real-time (that is to say, present) set ofdata (also acquired through the wearable electronic device 100), aphysician or other caregiver C can quickly ascertain if significantchanges in a monitored individual's activity or other behavior warrantadditional caregiver C intervention. For example, if a model (such asthe one or more machine learning models discussed herein) makes adetermination from the data acquired through the wearable electronicdevice 100 that the likelihood of a development of or worsening of theseor other health conditions is imminent, an alert may be sent to the oneor more remote computing devices 900 that are accessible by thecaregivers C.

As such, the wearable electronic device 100 in one form acts as thecornerstone of not just CDS or diagnosis of a person P with imminentchanges in health condition based on the acquired LEAP data, but also asa way for one caregiver C (for example, nurses, home health clinicians,assisted living facility personnel or the like) to convey the results ofhis or her patient evaluation to other caregivers C (for example, thephysician) in a cross-disciplinary manner in order to help the latterassess whether significant deviations from the patient's healthcondition norms are worthy of changes in a treatment regimen for thepatient. In one form, the LEAP data may form some or all of the dataavailable to the caregiver C that in turn may be used as part of aSituation, Background, Assessment, Recommendation (SBAR) or other bestpractice-based clinical guideline such as those published by the JointCommission, the Center for Disease Control (CDC), National Institute ofHealth (NIH), or other professional health-related societies. Within thepresent disclosure, it is the use of the SBAR's assessment componentthat is of most interest as the various pieces of patient informationacquired by the LEAP data of the wearable electronic device 100,including vital signs such as temperature, pulse, respiration rate,blood pressure, O₂ saturation or the like, dyspnea, cough, fatigue,restlessness, anxiousness, confusion, anxiety or the like, may be usedto complement the situation and background information as a way to moreeffectively convey such information to a physician or other caregiver Cthat is qualified to make a recommendation for the treatment of thepatient, such as forming a patient action plan for the person Passociated with the wearable electronic device 100.

The Food and Drug Administration (FDA) classifies software as a medicaldevice (SaMD) into categories based on risk and intended purpose. The21st Century Cures Act states that certain medical software is no longerconsidered to be regulated as a medical device, including that whichprovides limited CDS. On the other hand, software that allows imageviewing for the purpose of making a diagnosis, software that offerstreatment planning and software that is connected to a hardware medicaldevice but is not needed by that hardware medical device to achieve itsintended medical purpose that is not an accessory to the hardwaremedical device may be deemed to be SaMD.

In use, one or more signals associated with the LEAP data are detectedby one or more of the sensors 121 and the first and second wirelesscommunication sub-modules 175A, 175B. These and additional data—as wellas the inferring of one or more criteria associated with a particularhealth condition—may be used to provide CDS that in turn may correlateto an action plan or related therapy recommendation. As such, the outputfrom a machine learning model as discussed herein may be in the form ofcontent variable, control variables or both. In such event, the outputis not out of necessity mutually exclusive as some output may be usedfor CDS and actual diagnoses and attendant action plans, as well as forone or the other.

Thus, and in addition to helping with retrospective-oriented clinicalanalytics (that is to say, looking backwards to see what change inhealth condition event has already happened), the use of one or more ofthe machine learning models as discussed herein and that are acting uponthe LEAP data acquired from the wearable electronic device 100 may alsohelp with predictive analytics in order to understand what change inhealth condition event is likely to happen, as well as prescriptiveanalytics in order to form an action plan to mitigate or prevent anyadverse changes in patient health. In one form, the use of neuralnetworks, K-means clustering or other machine learning models asdiscussed herein may be used to perform diagnostics as a service (DaaS)such that it has SaMD functionality. The capability of such a predictiveservice may be used not only to diagnose (or help diagnose) certainadverse health conditions such as UTIs or cognitive or neuropsychiatriccondition, but also to determine other health-related metrics asdiscussed previously, such as the chance of readmission to a hospital,length of hospital stays or the like. In another form (such as wherediagnostics that leads to prescriptive analytics in order to form anaction plan is avoided), SaMD functionality may be avoided.

Conclusion

The use of one or both of the wearable electronic device 100 and theaccompanying system 1 to infer indicators of changing health of thewearer of the device through the monitoring of one or more forms of LEAPdata is described herein. Besides using the wearable electronic device100 for analyzing the change in an individual's health condition, otherapplications, such as for firefighters, schools (particularly those thatdeal with autistic children), military personnel and prison inmates, arealso within the scope of the present disclosure. References in thepresent disclosure to the various forms, as well as to thepreviously-mentioned aspects, are meant to indicate that such forms oraspects may include one or more particular features, structures orrelated characteristics, but that each such form or aspect need notnecessarily include all such particular features, structures orcharacteristics, and that any claimed combination of such features,structures or characteristics in part or in their entirety as describedherein is has a basis in—and is therefore deemed to be within the scopeof—the present disclosure.

Within the present disclosure, the acquisition of one or more portionsof the LEAP data the wearable electronic device 100, as well as thepossible subsequent use of such LEAP data (such as by the machinelearning algorithms and models discussed herein) will be understood toinclude all or a portion of such data, either in the aggregate (that isto say, of all of the location, environmental, activity andphysiological components), individually (that is to say, of any one ofthe location, environmental, activity and physiological components) orin combination (that is to say, of two or more of the location,environmental, activity and physiological components) unless the contextspecifically dictates otherwise. Likewise, such acquisition and possiblesubsequent use of such LEAP data will be understood to include all or aportion of the data collected within any one of the location,environmental, activity and physiological components, also only unlessthe context specifically dictates otherwise.

Within the present disclosure, data-driven features of interest (such asthose used to infer one or more of the health conditions, behaviorpatterns or the like as described herein) may in one form be derived,processed, computed or otherwise established through the uniquecooperation of the various structures described herein, including thedata structures and program structures that may be embodied incorresponding machine code 173E that in turn may be operated upon by thecomputer-based logic device 173. In one form, the various operationsperformed by such machine code 173E and logic device 173 may beconceptually grouped into various modules for convenience ofcategorization purposes with the understanding that such grouping doesnot change the structural cooperation between such code and such deviceor devices. As such, certain operations—such as those associated withvarious portions of the five-step machine learning workflow 1000 asdescribed herein—may conceptually be described or otherwise grouped intomodular format without departing from the scope or intent of the presentdisclosure.

Within the present disclosure, it will be understood that sections,headings and sub-headings that are used herein are included forreference and to assist the reader with locating various sections. Assuch, these headings are not intended to limit the scope of the conceptsdescribed with respect thereto, and that such concepts may haveapplicability throughout some or all of the present disclosure.

Within the present disclosure, the term “patient” is meant to include aperson (such as person P of FIGS. 4 and 5) who is either undershort-term or long-term in-patient or out-patient care of a doctor,nurse or other professional caregiver C within a hospital or doctor'soffice, as well as a person P who either resides at home under a homehealth-care model, or is a resident either at home or within an assistedliving model or related long-term or short-term care model. In addition,the term may be applied to a person P who is in need of health orlocation monitoring through the wearable electronic device 100,regardless of whether such person P is or is not under the present careof a doctor, nurse or other professional caregiver C. Accordingly, thevarious terms used herein to identify the wearer of the wearableelectronic device 100 as a “person”, “user”, “individual” or “patient”are deemed to be equivalents within the present disclosure, and that anygreater degree of specificity of such terms will be apparent from thecontext.

Within the present disclosure, it will be understood that theoperations, functions, logical blocks, modules, circuits, and algorithmor model steps or events described may be implemented in hardware,software, firmware or any combination thereof. Moreover, if implementedin software, such operations may be stored on or transmitted over as oneor more instructions or code on a computer-readable medium. The steps orevents of a method, algorithm or ensuing model disclosed herein may beembodied in a processor-executable software module, which may reside ona tangible, non-transitory version of such computer-readable storagemedium such that the medium be in any available form that permits accessto the events or steps by a processor or related part of a computer. Byway of example, and not limitation, such non-transitorycomputer-readable medium may comprise RAM, ROM, EEPROM, CD-ROM or otheroptical disk storage, magnetic disk storage or other magnetic storagedevices, flash memory or any other form that may be used to storedesired program code in the form of instructions or data structures andthat may be accessed by a processor or related part of a computer.Combinations of the above should also be included within the scope ofnon-transitory computer-readable media. Additionally, the operations ofa method, algorithm or model may reside as one or any combination or setof codes or instructions on a tangible, non-transitory machine readablemedium or computer-readable medium, which may be incorporated into acomputer program product.

Within the present disclosure, one or more of the following claims mayutilize the term “wherein” as a transitional phrase. For the purposes ofdefining features discussed in the present disclosure, this term isintroduced in the claims as an open-ended transitional phrase that isused to introduce a recitation of a series of characteristics of thestructure and should be interpreted in like manner as the more commonlyused open-ended preamble term “comprising.”

Within the present disclosure, terms such as “preferably”, “generally”and “typically” are not utilized to limit the scope of the claims or toimply that certain features are critical, essential, or even importantto the disclosed structures or functions. Rather, these terms are merelyintended to highlight alternative or additional features that may or maynot be utilized in a particular embodiment of the disclosed subjectmatter. Likewise, it is noted that the terms “substantially” and“approximately” and their variants are utilized to represent theinherent degree of uncertainty that may be attributed to anyquantitative comparison, value, measurement or other representation. Assuch, use of these terms represents the degree by which a quantitativerepresentation may vary from a stated reference without resulting in achange in the basic function of the subject matter at issue.

Within the present disclosure, the use of the prepositional phrase “atleast one of” is deemed to be an open-ended expression that has bothconjunctive and disjunctive attributes. For example, a claim that states“at least one of A, B and C” (where A, B and C are definite orindefinite articles that are the referents of the prepositional phrase)means A alone, B alone, C alone, A and B together, A and C together, Band C together, or A, B and C together. By way of example within thepresent context, if a claim recites that data is being acquired from atleast one of a first wireless communication sub-module and a secondwireless communication sub-module, and if such data is being acquiredfrom the first wireless communication sub-module alone, the secondwireless communication sub-module alone or both of the first and secondwireless communication sub-modules, then such data acquisition satisfiesthe claim.

Within the present disclosure, the following claims are not written inmeans-plus-function format and are not intended to be interpreted basedon 35 USC 112(f) unless and until such claim limitations expressly usethe phrase “means for” followed by a statement of function void offurther structure.

Having described the subject matter of the present disclosure in detailand by reference to specific embodiments, it is noted that the variousdetails disclosed in the present disclosure should not be taken to implythat these details relate to elements that are essential components ofthe various described embodiments, even in cases where a particularelement is illustrated in each of the drawings that accompany thepresent description. Further, it will be apparent that modifications andvariations are possible without departing from the scope of the presentdisclosure, including, but not limited to, embodiments defined in theappended claims. More specifically, although some aspects of the presentdisclosure may be identified as preferred or particularly advantageous,it is contemplated that the present disclosure is not necessarilylimited to these aspects.

It will be apparent to those skilled in the art that variousmodifications and variations can be made to the described embodimentswithout departing from the spirit and scope of the claimed subjectmatter. Thus it is intended that the specification cover themodifications and variations of the various described embodimentsprovided such modification and variations come within the scope of theappended claims and their equivalents.

What is claimed is:
 1. A method of monitoring an individual, the methodcomprising: configuring at least one of a plurality of wirelessintermediate links within a wireless intermediate link network tooperate as a connection node to receive from an end node that is securedto the individual a wireless signal that is being transmitted using alow power wide area network protocol and which comprises data that hasbeen acquired by the end node, wherein at least one of the connectionnode and at least one other of the plurality of wireless intermediatelinks is further configured to perform node-to-node communication withat least one other of the plurality of wireless intermediate linkswithin the wireless intermediate link network; and upon receipt of thedata by the connection node, transmitting, by at least one of theconnection node and at least one of the plurality of wirelessintermediate links within the wireless intermediate link network, atleast a portion of the received data to at least one of a monitoringdevice and a monitoring individual through a backhaul computer network.2. The method of claim 1, wherein the node-to-node communication withinthe wireless intermediate link takes place only upon the inaccessibilityof at least one of the plurality of wireless intermediate links toachieve signal communication with the backhaul computer network.
 3. Themethod of claim 2, wherein at least a portion of a time of theinaccessibility takes place while the at least one of the plurality ofwireless intermediate links is forwarding the received data.
 4. Themethod of claim 1, wherein the backhaul computer network comprises anetwork server and at least one application server.
 5. The method ofclaim 4, wherein the transmitting to the network server is through atleast one of an IP protocol, a cellular protocol and a WiFi protocol. 6.The method of claim 1, wherein the plurality of wireless intermediatelinks comprises a plurality of gateways each of which is configured toexchange the data that has been acquired by the end node in the form ofa packet such that the plurality of gateways operate aspacket-forwarding devices.
 7. The method of claim 1, wherein at leastone of the connection node and the end node are configured as a wearableelectronic device that comprises a hybrid wireless communication modulecomprising, in addition to a sub-module for transmitting the wirelesssignal using the low-power wide area network protocol, a sub-module forreceipt of location data in the form of a beacon signal and a sub-modulefor receipt of location data in the form of a global navigationsatellite system signal.
 8. The method of claim 7, wherein the end nodethat is configured as the wearable electronic device is one of aplurality of wearable electronic devices each of which is configured tosignally perform node-to-node communication with at least one other ofthe plurality of wearable electronic devices such that a wearableelectronic device network is formed between them.
 9. The method of claim1, wherein at least one of the plurality of wireless intermediate linkscomprises a gateway and at least one of the plurality of wirelessintermediate links comprises a wireless electronic device that isconfigured to have substantially similar signal transmitting capabilityto that of the end node.
 10. The method of claim 9, wherein a pluralityof the wireless intermediate links comprise gateways each of whichcomprises a network server such that the wireless intermediate linknetwork defines a private network.
 11. The method of claim 1, whereinthe data that has been acquired by the end node and received by theconnection node comprises at least one of location data, environmentaldata, activity data and physiological data.
 12. (canceled) 13.(canceled)
 14. The method of claim 11, further comprising using aplurality of sensors signally cooperative with the end node to acquireat least one of the location data, environmental data, activity data andphysiological data.
 15. The method of claim 11, further comprisinggenerating a report corresponding to at least one of a health conditionand a location of the individual based on correlating the at least oneof the location data, environmental data, activity data andphysiological data to a respective one of the health condition andlocation.
 16. The method of claim 11, further comprising analyzing atleast a portion of the at least one of the location data, environmentaldata, activity data and physiological data using a machine learningmodel that has been trained by a machine learning algorithm and at leasta portion of the at least one of the location data, environmental data,activity data and physiological data.
 17. The method of claim 16,wherein the machine learning algorithm used for training the machinelearning model takes place in the backhaul computer network andcomprises: segmenting at least a portion of the at least one of thelocation data, environmental data, activity data and physiological datainto a training set; using the training set in the machine learningalgorithm in order to generate an initial fit of the machine learningmodel; segmenting at least a portion of the at least one of the locationdata, environmental data, activity data and physiological data into avalidation set; using the validation set to in order to update theinitial fit; and iterating the machine learning algorithm using thetraining and validation sets to reduce validation errors in order togenerate a final fit of the machine learning model.
 18. The method ofclaim 16, wherein the machine learning model that has been trained isperformed on the end node.
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 34. The method of claim 16,wherein the machine learning model that has been trained is performed onat least one of the plurality of wireless intermediate links. 35.(canceled)
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 37. The method of claim 1, wherein the lowpower wide area network protocol that is used by the end node comprisesa LoRaWAN-based protocol.
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 42. The method of claim 1, wherein the wireless signalbeing transmitted from the end node to the at least one of the pluralityof wireless intermediate links comprises a chirp spread spectrum-basedsignal.
 43. The method of claim 1, wherein the wireless signal that isbeing transmitted using a low power wide area network protocol ismodulated using frequency shift keying.
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